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ahmedelsayed
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videos

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	great um yeah so today we're going to be

	2
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	talking a little bit about generation

	3
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	algorithms um this will be sort of a

	4
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	tour through some of the most common

	5
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	methods and we're going to talk a little

	6
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	bit about the theory behind them as well

	7
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	um if you're looking at the slides on

	8
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	the website these might be ever so

	9
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	slightly different um but yeah I'll try

	10
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	to stop at each section boundary for

	11
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	questions also feel free to sort of

	12
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	interrupt at any point for

	13
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	clarifications so we're starting off

	14
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	today with some great news um let's say

	15
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	that you have some friend who maybe owns

	16
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	a giant tech company and they've gifted

	17
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	you this absolutely massive new model M

	18
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	um it's a great model it's pre-trained

	19
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	with the latest architecture it's

	20
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	pre-trained on um trillions of tokens of

	21
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	text it's got seven billion parameters

	22
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	it looks like a really promising new

	23
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	model you know it's the top of all these

	24
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	leaderboards um but if you actually take

	25
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	your new model M and you sort of open up

	26
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	this box and kind of Shake It Out maybe

	27
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	from last class you know a little bit

	28
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	architecturally what this model might

	29
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	look like but if you actually kind of

	30
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	take a closer look at it from a

	31
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	different angle what you see is that m

	32
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	is actually just a conditional

	33
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	probability distribution um you put some

	34
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	input X into your model and you get some

	35
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	probability out for any given sequence

	36
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	that you're sort of interested in

	37
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	evaluating right um and in particular M

	38
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	gives you a probability distribution

	39
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	over all tokens in its vocabulary to

	40
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	predict like what token you would output

	41
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	next right and so this is what this

	42
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	equation says um given some input X and

	43
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	everything that you've predicted so far

	44
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	you get the probability of the next

	45
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	token in YJ and if you multiply this out

	46
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	over all the probabilities in your

	47
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	sequence you can calculate the

	48
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	probability of any output y given your

	49
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	input X so what this like super fancy

	50
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	model that you spend a lot of money to

	51
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	train is really just a conditional

	52
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	probability distribution um but this

	53
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	turns out to be okay because you can use

	54
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	a conditional probability distribution

	55
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	to do sort of any task that we're really

	56
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	interested in in NLP um pretty much any

	57
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	task right so by changing what you

	58
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	consider your input X and your output y

	59
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	to be you can can get outputs from this

	60
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	model for things like translation for

	61
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	summarization for reasoning Tas um just

	62
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	by sort of changing what you consider

	63
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	your inputs and outputs in this

	64
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	setting but there's sort of both good

	65
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	and bad things about your model being a

	66
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	probability distribution instead of just

	67
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	an oracle that gives you sort of a

	68
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	single answer for every input um one

	69
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	kind of nice thing about this

	70
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	distribution um is that you can get at

	71
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	an idea of something like confidence

	72
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	right if you give your model the input 2

	73
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	plus 2 equals and almost all the

	74
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	probability mass is on the token of four

	75
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	you can say like the model predicts with

	76
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	pretty high confidence that 2 plus 2

	77
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	equals four um versus if you give it

	78
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	something that's maybe a little more

	79
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	open-ended like you ask it to predict

	80
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	Graham's favorite color and you see this

	81
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	distribution that's sort of a lot

	82
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	flatter you know the most likely output

	83
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	is green but maybe we don't have a lot

	84
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	of confidence that that's the correct

	85
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	answer um this is really closely tied

	86
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	into the idea of calibration which you

	87
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	guys talked about um I guess a couple of

	88
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	classes ago now the flip side of this

	89
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	though is that you know Noti that for

	90
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	this case like 2 plus 2al 4 not all of

	91
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	the probability mass is on four um and

	92
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	so models that are conditional

	93
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	probability distributions can

	94
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	hallucinate right um pretty much no

	95
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	matter what you do there's going to be

	96
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	some nonzero probability to some output

	97
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	that's incorrect or

	98
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	undesirable um in some cases maybe even

	99
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	offensive something that you don't want

	100
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	the model to Output um and this is sort

	101
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	of an artifact of the way these models

	102
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	are trained if there's some great work

	103
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	kind of more on the theory side here

	104
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	that shows that this is actually true

	105
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	even if everything in your input

	106
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	training data is sort of correct and

	107
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	factual and doesn't have any errors

	108
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	you'll still wind up with a situation

	109
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	where some nonzero probability mass is

	110
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	on some outputs that are undesirable or

	111
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	hallucinatory for sort of most inputs

	112
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	that you care about evaluating so if we

	113
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	have these issues how do we actually get

	114
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	a good output out of the model um and to

	115
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	do that we're first going to talk about

	116
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	some sampling methods um but I want to

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	pause here in case there are of any

	118
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	questions on this idea of a model is a

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	conditional

	120
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	distribution great so we can jump right

	121
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	in so we have this model right we know

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	at each step at each token we might want

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	to decode the distribution of likelihood

	124
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	over all vocabulary tokens right this

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	conditional distribution we've been

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	talking about um for the next time step

	127
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	and what we want out of this is a good

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	output um for some definition of good

	129
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	that we can sort of develop as we go

	130
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	here so maybe the natural first thing to

	131
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	try is we have a probability

	132
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	distribution can we just sample from it

	133
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	right and this is something called

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	ancestral sampling so at each time step

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	we're going to draw a token from this

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	distribution sort of according to its

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	relative probability right so if

	138
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	something has twice as much probability

	139
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	Mass according to the model we'll draw

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	it twice as often um and we can sample

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	from this distribution at each time step

	142
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	and this is sort of this is sort of a

	143
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	nice setup um we get exact samples from

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	the model distribution so using the

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	setup if you can you imagine like

	146
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	drawing an almost infinite number of

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	samples like a ridiculously large number

	148
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	and you look at their probabilities

	149
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	you'd sort of get something from this

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	distribution with exactly the

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	probability that the real model

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	distribution is given you um so this is

	153
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	great this gives us an exact sample from

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	the model this seems to be exactly what

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	we want um but you can guess probably by

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	the fact that we're only like 10 minutes

	157
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	into class here this is not really the

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	end of the story um and there's actually

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	a couple of problems with sampling

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	directly from our model distribu

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	the one that we're really going to focus

	162
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	on first here is this idea of a long

	163
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	tail so a model like llama and maybe our

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	new model M um has 32,000 vocabulary

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	tokens and you can imagine maybe out of

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	those tokens there might be one or even

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	2,000 of those tokens that are sort of a

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	reasonable next thing to predict for a

	169
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	really open-ended task right but there's

	170
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	going to be all kinds of things in that

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	distribution um that are maybe like

	172
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	punctuation there maybe tokens that

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	won't actually lead to the correct

	174
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	answer like there's a lot of things in

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	this distribution that would be all

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	really low likelihood and this is fine

	177
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	these things just get low probability

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	Mass but the problem is if you give sort

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	of a small amount of probability Mass to

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	30,000 different things that mass will

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	add up pretty quickly um and to see this

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	we have sort of this illustration here

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	um I don't know if you can see the

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	difference between the green and the

	185
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	yellow but I've also drawn a little bar

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	between them this is a really longtailed

	187
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	distribution and the green part of the

	188
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	distribution which is a lot of tokens

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	with high likelihood has 50% of the

	190
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	total probability the Yellow Part which

	191
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	is all a lot of things that are all

	192
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	individually not super likely is the

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	other 50% of the probability and so what

	194
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	that means is if you're doing something

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	like ancestral sampling 50% of the time

	196
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	you'll be sampling something really

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	unlikely from this long tail um that

	198
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	seems sort of not like what we want

	199
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	right um so is there anything we can do

	200
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	about this and the obvious for solution

	201
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	here is can we just cut off that tail

	202
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	like if we know these tokens are not

	203
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	super likely can we just ignore them and

	204
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	there's a couple of different ways to do

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	that um the first of these is something

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	called topk sampling where we say okay

	207
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	you know maybe we think there are 10

	208
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	reasonable like outputs is right maybe

	209
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	we'll just sample from the 10 most

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	probable tokens um here maybe we say if

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	we want to pick top six sampling we'll

	212
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	sample from just the six most probable

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	tokens and so in this example you can

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	see we originally had 10 tokens and

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	we're going to sample from just the blue

	216
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	ones just the six most likely tokens

	217
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	um in this example this distribution is

	218
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	pretty flat there's a lot of things that

	219
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	are like kind of likely right so that

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	those six tokens are only 68% of the

	221
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	total probability Mass um if we go like

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	one time step further here we might have

	223
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	a distribution that's a lot peier most

	224
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	of the mass is on just a single token

	225
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	and so sampling from just the top six

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	tokens actually captures 99% of the

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	probability mes maybe we say that seems

	228
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	a little excessive right we don't really

	229
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	need um maybe all of these tokens that

	230
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	are all kind of low probability maybe we

	231
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	just want to sort of sample from the top

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	half of our distribution or something or

	233
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	the top

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	90% um so instead of choosing a top

	235
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	number of tokens to sample from you

	236
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	could choose a top amount of probability

	237
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	and this is something called top P or

	238
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	nucleus sampling so P here is the amount

	239
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	of probability from your distribution

	240
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	you want to consider so if you decide

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	your p is about like 94% of the

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	probability Mass you in this first examp

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	example here would choose almost all of

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	the tokens you keep adding tokens in

	245
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	until you reach an amount of total

	246
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	probability that's about

	247
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	094 but then when you get to the Second

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	Step where you have a couple of really

	249
	00:08:40,880 --> 00:08:45,959
	highly probable tokens you'd only need a

	250
	00:08:43,240 --> 00:08:47,959
	couple of tokens to add up to 094 or

	251
	00:08:45,959 --> 00:08:50,320
	even higher than 0.94 and so you would

	252
	00:08:47,959 --> 00:08:52,200
	just sample from a smaller set of tokens

	253
	00:08:50,320 --> 00:08:54,600
	so in top K sampling the total amount of

	254
	00:08:52,200 --> 00:08:56,560
	probability your sampling from can move

	255
	00:08:54,600 --> 00:08:58,120
	around in top P sampling the total

	256
	00:08:56,560 --> 00:08:59,839
	number of tokens you're sampling from

	257
	00:08:58,120 --> 00:09:01,959
	might change

	258
	00:08:59,839 --> 00:09:04,760
	um but maybe we sort of don't want to

	259
	00:09:01,959 --> 00:09:07,279
	impose a strong constraint like we want

	260
	00:09:04,760 --> 00:09:09,279
	like 94% here maybe just what we really

	261
	00:09:07,279 --> 00:09:11,040
	care about is saying that we're not

	262
	00:09:09,279 --> 00:09:14,000
	going to sample anything that's really

	263
	00:09:11,040 --> 00:09:16,800
	really unlikely right another way of

	264
	00:09:14,000 --> 00:09:18,560
	doing this is called Epsilon sampling

	265
	00:09:16,800 --> 00:09:20,519
	where we just sample tokens that have at

	266
	00:09:18,560 --> 00:09:22,920
	least some minimum amount of probability

	267
	00:09:20,519 --> 00:09:24,720
	to them right so maybe we just want

	268
	00:09:22,920 --> 00:09:29,519
	tokens that have probability of at least

	269
	00:09:24,720 --> 00:09:31,240
	0.05 here um in this first um example

	270
	00:09:29,519 --> 00:09:32,640
	everything has at least some reasonable

	271
	00:09:31,240 --> 00:09:34,240
	amount of probability so we're actually

	272
	00:09:32,640 --> 00:09:36,240
	going to sample from our full

	273
	00:09:34,240 --> 00:09:37,720
	distribution and then in the second

	274
	00:09:36,240 --> 00:09:39,279
	example when we have a lot of things

	275
	00:09:37,720 --> 00:09:41,160
	that are really unlikely we'll only

	276
	00:09:39,279 --> 00:09:43,800
	sample from sort of the more likely part

	277
	00:09:41,160 --> 00:09:45,240
	of the distribution um so all three of

	278
	00:09:43,800 --> 00:09:47,000
	these methods are sort of different ways

	279
	00:09:45,240 --> 00:09:49,399
	of trying to cut off the long tail using

	280
	00:09:47,000 --> 00:09:51,480
	sort of different

	281
	00:09:49,399 --> 00:09:53,000
	characteristics the tail of the

	282
	00:09:51,480 --> 00:09:55,680
	distribution though isn't the only thing

	283
	00:09:53,000 --> 00:09:58,000
	we could choose to modify um we could

	284
	00:09:55,680 --> 00:09:59,880
	also choose to modify this sort of

	285
	00:09:58,000 --> 00:10:02,120
	peakiness of the distribution

	286
	00:09:59,880 --> 00:10:03,880
	so if you look here at the middle of

	287
	00:10:02,120 --> 00:10:06,600
	these diagrams say this is your original

	288
	00:10:03,880 --> 00:10:08,519
	distribution over next tokens and maybe

	289
	00:10:06,600 --> 00:10:11,040
	you want to modify some properties of

	290
	00:10:08,519 --> 00:10:12,640
	this distribution like you say I want an

	291
	00:10:11,040 --> 00:10:14,200
	output that's really diverse and

	292
	00:10:12,640 --> 00:10:15,680
	interesting and open-ended like maybe

	293
	00:10:14,200 --> 00:10:17,920
	this is something like story generation

	294
	00:10:15,680 --> 00:10:20,120
	where you want to have sort of a lot of

	295
	00:10:17,920 --> 00:10:21,279
	maybe surprising things in your output

	296
	00:10:20,120 --> 00:10:23,480
	you could say I want to sort of

	297
	00:10:21,279 --> 00:10:26,440
	distribute my probability Mass more over

	298
	00:10:23,480 --> 00:10:28,399
	the token space and you can do this um

	299
	00:10:26,440 --> 00:10:32,720
	by sort of flattening this distribution

	300
	00:10:28,399 --> 00:10:34,240
	like you see on the the right here um

	301
	00:10:32,720 --> 00:10:36,800
	where now there's sort of more

	302
	00:10:34,240 --> 00:10:39,040
	probability Mass spread over this um

	303
	00:10:36,800 --> 00:10:40,320
	like wider set of tokens you could also

	304
	00:10:39,040 --> 00:10:42,720
	say the opposite right you could say

	305
	00:10:40,320 --> 00:10:44,120
	maybe I'm doing something like math

	306
	00:10:42,720 --> 00:10:45,519
	where there shouldn't really be a lot of

	307
	00:10:44,120 --> 00:10:47,800
	correct answers there should be really

	308
	00:10:45,519 --> 00:10:50,399
	only one or maybe only like a few

	309
	00:10:47,800 --> 00:10:52,320
	potential reasonable next answers and so

	310
	00:10:50,399 --> 00:10:54,160
	you can make your distribution peier or

	311
	00:10:52,320 --> 00:10:56,639
	sharper so that more of the probability

	312
	00:10:54,160 --> 00:11:00,200
	mass is on the things at the very top um

	313
	00:10:56,639 --> 00:11:02,000
	the way you do this is you modify y your

	314
	00:11:00,200 --> 00:11:04,320
	loges your outputs of the last layer of

	315
	00:11:02,000 --> 00:11:06,399
	the model before you apply softn so when

	316
	00:11:04,320 --> 00:11:08,360
	you're predicting you get your outputs

	317
	00:11:06,399 --> 00:11:10,040
	of the last layer of the model and then

	318
	00:11:08,360 --> 00:11:11,560
	you apply softmax which turns those

	319
	00:11:10,040 --> 00:11:15,240
	outputs into a distribution right they

	320
	00:11:11,560 --> 00:11:17,399
	all sum up the um like Mass over all

	321
	00:11:15,240 --> 00:11:18,839
	vocabulary tokens sums to one and so

	322
	00:11:17,399 --> 00:11:21,920
	that is sort of a distribution you could

	323
	00:11:18,839 --> 00:11:23,519
	sample from if you divide those Logics

	324
	00:11:21,920 --> 00:11:26,000
	by some number before you apply that

	325
	00:11:23,519 --> 00:11:27,880
	softmax you can make that distribution

	326
	00:11:26,000 --> 00:11:30,760
	flatter by using a number greater than

	327
	00:11:27,880 --> 00:11:32,440
	one or peier by using a number less than

	328
	00:11:30,760 --> 00:11:35,079
	one and this is this type of parameter

	329
	00:11:32,440 --> 00:11:36,839
	is called temperature um you can apply

	330
	00:11:35,079 --> 00:11:38,480
	this with any of the other methods for

	331
	00:11:36,839 --> 00:11:40,279
	sort of cutting off the long tail but

	332
	00:11:38,480 --> 00:11:41,920
	what people will often do is just apply

	333
	00:11:40,279 --> 00:11:43,639
	a temperature and then sample from that

	334
	00:11:41,920 --> 00:11:45,320
	distribution and that's what we call

	335
	00:11:43,639 --> 00:11:48,720
	temperature

	336
	00:11:45,320 --> 00:11:49,920
	sampling so these I think most of you

	337
	00:11:48,720 --> 00:11:51,320
	might already have been at least a

	338
	00:11:49,920 --> 00:11:53,000
	little bit familiar with some of these

	339
	00:11:51,320 --> 00:11:56,079
	methods I want to touch briefly on a

	340
	00:11:53,000 --> 00:11:58,160
	couple of other ideas for modifying this

	341
	00:11:56,079 --> 00:11:59,680
	distribution maybe some more complex and

	342
	00:11:58,160 --> 00:12:01,839
	more recent ideas and the one that I

	343
	00:11:59,680 --> 00:12:04,279
	want to talk about in more detail is

	344
	00:12:01,839 --> 00:12:05,399
	something called contrastive decoding so

	345
	00:12:04,279 --> 00:12:07,360
	the idea here is that we could

	346
	00:12:05,399 --> 00:12:10,800
	incorporate some extra information at

	347
	00:12:07,360 --> 00:12:12,760
	decoding time um using some other

	348
	00:12:10,800 --> 00:12:15,320
	distribution some other data or in this

	349
	00:12:12,760 --> 00:12:17,320
	case some other model so if you've ever

	350
	00:12:15,320 --> 00:12:19,240
	played around with a really like

	351
	00:12:17,320 --> 00:12:21,800
	relatively small language model maybe

	352
	00:12:19,240 --> 00:12:23,320
	something like gbt2 small um You

	353
	00:12:21,800 --> 00:12:26,560
	probably noticed you try to give it some

	354
	00:12:23,320 --> 00:12:28,240
	inputs and maybe it degenerates into

	355
	00:12:26,560 --> 00:12:30,160
	just repeating the same sequence over

	356
	00:12:28,240 --> 00:12:31,720
	and over maybe it gives you outputs that

	357
	00:12:30,160 --> 00:12:33,399
	are just completely incorrect like you

	358
	00:12:31,720 --> 00:12:35,320
	ask it a factual question and it gets it

	359
	00:12:33,399 --> 00:12:37,120
	wrong um and you don't see those

	360
	00:12:35,320 --> 00:12:39,519
	problems if you look at sort of a larger

	361
	00:12:37,120 --> 00:12:41,399
	model that's trained on more data so the

	362
	00:12:39,519 --> 00:12:43,199
	question here is can you use what that

	363
	00:12:41,399 --> 00:12:46,480
	smaller model is getting wrong to make

	364
	00:12:43,199 --> 00:12:49,120
	your larger model even better um and the

	365
	00:12:46,480 --> 00:12:51,360
	way we do this is by sort of the

	366
	00:12:49,120 --> 00:12:52,880
	intuition that if the smaller model

	367
	00:12:51,360 --> 00:12:55,079
	doesn't have a lot of probability on

	368
	00:12:52,880 --> 00:12:57,160
	some answer but the the larger model

	369
	00:12:55,079 --> 00:12:58,519
	does it's likely because that larger

	370
	00:12:57,160 --> 00:13:02,279
	model has learned something with the

	371
	00:12:58,519 --> 00:13:04,000
	smaller model didn't know and so here we

	372
	00:13:02,279 --> 00:13:06,199
	modify the probability distribution

	373
	00:13:04,000 --> 00:13:08,199
	coming out of the larger model to choose

	374
	00:13:06,199 --> 00:13:11,120
	outputs that that model thinks are very

	375
	00:13:08,199 --> 00:13:12,600
	likely and the amateur or the the weaker

	376
	00:13:11,120 --> 00:13:15,480
	model thinks are not

	377
	00:13:12,600 --> 00:13:20,000
	likely so in this example here from

	378
	00:13:15,480 --> 00:13:22,560
	their paper um if you have sort of a

	379
	00:13:20,000 --> 00:13:27,199
	input like Barack Obama was born in

	380
	00:13:22,560 --> 00:13:29,720
	Hawaii he was born in L um the smaller

	381
	00:13:27,199 --> 00:13:31,360
	model would often do something like

	382
	00:13:29,720 --> 00:13:35,399
	start repeating and actually if you

	383
	00:13:31,360 --> 00:13:36,720
	sample sort of naively from the um

	384
	00:13:35,399 --> 00:13:38,560
	larger model you can wind up in these

	385
	00:13:36,720 --> 00:13:40,000
	situations as well right so if you just

	386
	00:13:38,560 --> 00:13:41,959
	choose the most likely thing at each

	387
	00:13:40,000 --> 00:13:43,399
	step you wind up in this Loop where it's

	388
	00:13:41,959 --> 00:13:45,560
	like he was born in Hawaii he was born

	389
	00:13:43,399 --> 00:13:48,199
	in Hawaii he was born in Hawaii um and

	390
	00:13:45,560 --> 00:13:51,320
	this is behavior we generally don't want

	391
	00:13:48,199 --> 00:13:52,680
	um if you do something like nucleus or

	392
	00:13:51,320 --> 00:13:53,720
	top PE sampling you can wind up with

	393
	00:13:52,680 --> 00:13:55,880
	things that are actually completely

	394
	00:13:53,720 --> 00:13:58,839
	incorrect like he was born in Washington

	395
	00:13:55,880 --> 00:14:01,480
	DC um but if you use contrastive

	396
	00:13:58,839 --> 00:14:04,120
	decoding you take the outputs coming out

	397
	00:14:01,480 --> 00:14:05,720
	of your expert model here and you

	398
	00:14:04,120 --> 00:14:07,680
	subtract out the probabilities coming

	399
	00:14:05,720 --> 00:14:10,160
	out of the weaker model and you can wind

	400
	00:14:07,680 --> 00:14:11,880
	up with things that the higher model the

	401
	00:14:10,160 --> 00:14:13,759
	stronger model ascribed probability to

	402
	00:14:11,880 --> 00:14:15,480
	but the weaker model did not likely

	403
	00:14:13,759 --> 00:14:16,920
	because these are sort of facts that the

	404
	00:14:15,480 --> 00:14:18,959
	larger model knows that the smaller

	405
	00:14:16,920 --> 00:14:20,800
	model does not so here we actually get

	406
	00:14:18,959 --> 00:14:23,199
	the year Barack Obama was born which is

	407
	00:14:20,800 --> 00:14:25,800
	maybe a fact that the larger model knows

	408
	00:14:23,199 --> 00:14:27,639
	and the smaller model didn't know um and

	409
	00:14:25,800 --> 00:14:29,759
	so this is just one of sort of a broad

	410
	00:14:27,639 --> 00:14:32,560
	class of methods where you use external

	411
	00:14:29,759 --> 00:14:35,199
	information to improve your decoding by

	412
	00:14:32,560 --> 00:14:38,720
	modifying this distribution at each

	413
	00:14:35,199 --> 00:14:40,720
	set um those are sort of a brief tour of

	414
	00:14:38,720 --> 00:14:43,920
	a couple of different sampling methods

	415
	00:14:40,720 --> 00:14:43,920
	before we move into search

	416
	00:14:44,600 --> 00:14:50,440
	yeah

	417
	00:14:46,279 --> 00:14:54,880
	yeah is it going to improve upon just

	418
	00:14:50,440 --> 00:14:57,240
	the yeah it generally does um and the

	419
	00:14:54,880 --> 00:14:59,800
	intuition for why this might be I think

	420
	00:14:57,240 --> 00:15:01,680
	is that there are sort of these

	421
	00:14:59,800 --> 00:15:04,560
	degenerate cases like just repeating

	422
	00:15:01,680 --> 00:15:06,120
	over and over that both the expert and

	423
	00:15:04,560 --> 00:15:09,000
	the weak model would give relatively

	424
	00:15:06,120 --> 00:15:10,880
	high probability to um maybe the expert

	425
	00:15:09,000 --> 00:15:13,199
	model is like slightly less likely to do

	426
	00:15:10,880 --> 00:15:14,959
	these things but it's still like sort of

	427
	00:15:13,199 --> 00:15:16,639
	an easy case for the model to learn and

	428
	00:15:14,959 --> 00:15:18,120
	so both of those models will have high

	429
	00:15:16,639 --> 00:15:20,079
	probability for those things but the

	430
	00:15:18,120 --> 00:15:21,800
	things that are genuinely like good

	431
	00:15:20,079 --> 00:15:23,880
	outputs that only the expert would get

	432
	00:15:21,800 --> 00:15:25,519
	right those will have low probability

	433
	00:15:23,880 --> 00:15:27,600
	under the weak model and so you're sort

	434
	00:15:25,519 --> 00:15:30,880
	of subtracting out all the degenerate

	435
	00:15:27,600 --> 00:15:33,759
	behaviors and keeping to really good out

	436
	00:15:30,880 --> 00:15:35,240
	this if you're generating a longer

	437
	00:15:33,759 --> 00:15:37,440
	sequence with with

	438
	00:15:35,240 --> 00:15:40,759
	contacing how do you know which steps

	439
	00:15:37,440 --> 00:15:45,120
	you want to bring out yeah this is a

	440
	00:15:40,759 --> 00:15:48,560
	great question so for this particular

	441
	00:15:45,120 --> 00:15:50,560
	case oh yeah sorry so this was if you're

	442
	00:15:48,560 --> 00:15:52,279
	doing contrastive decoding over a really

	443
	00:15:50,560 --> 00:15:54,399
	long sequence like when do you choose to

	444
	00:15:52,279 --> 00:15:55,800
	bring in the expert right and for

	445
	00:15:54,399 --> 00:15:58,600
	contrastive decoding we're actually

	446
	00:15:55,800 --> 00:16:00,759
	going to do this at every individual

	447
	00:15:58,600 --> 00:16:02,440
	time step so we're going to use the

	448
	00:16:00,759 --> 00:16:04,800
	expert model to decode and we're going

	449
	00:16:02,440 --> 00:16:07,000
	to bring in the amateur to sort of

	450
	00:16:04,800 --> 00:16:09,079
	subtract out probabilities at each next

	451
	00:16:07,000 --> 00:16:10,399
	token prediction um you don't have to do

	452
	00:16:09,079 --> 00:16:12,800
	that I think that's that's what they do

	453
	00:16:10,399 --> 00:16:15,000
	in the paper um you could also decide to

	454
	00:16:12,800 --> 00:16:16,680
	only do this sort of if you have high

	455
	00:16:15,000 --> 00:16:19,639
	uncertainty or something if you don't

	456
	00:16:16,680 --> 00:16:22,639
	have a really sharp probability

	457
	00:16:19,639 --> 00:16:22,639
	distribution

	458
	00:16:23,160 --> 00:16:28,160
	yeah yeah how weak should the weak

	459
	00:16:25,399 --> 00:16:30,199
	predictor be um in the in the paper what

	460
	00:16:28,160 --> 00:16:31,600
	they're look at is actually not a huge

	461
	00:16:30,199 --> 00:16:34,560
	difference between the two models so you

	462
	00:16:31,600 --> 00:16:35,800
	can see here this is gpd2 XL and small

	463
	00:16:34,560 --> 00:16:37,319
	so there's a difference in parameter

	464
	00:16:35,800 --> 00:16:39,519
	counts and like a bit of a difference in

	465
	00:16:37,319 --> 00:16:42,160
	data I think here but these are actually

	466
	00:16:39,519 --> 00:16:44,959
	not like gpd2 XL is certainly not like a

	467
	00:16:42,160 --> 00:16:48,399
	super strong model now um I think they

	468
	00:16:44,959 --> 00:16:50,920
	try a couple of different settings and

	469
	00:16:48,399 --> 00:16:52,319
	the general intuition I think if I'm

	470
	00:16:50,920 --> 00:16:54,880
	remembering it correctly is that you

	471
	00:16:52,319 --> 00:16:56,319
	want a model that's not like so close in

	472
	00:16:54,880 --> 00:16:58,000
	performance to your expert that you're

	473
	00:16:56,319 --> 00:16:59,839
	basically just subtracting out useful

	474
	00:16:58,000 --> 00:17:02,240
	things but you also don't want a model

	475
	00:16:59,839 --> 00:17:03,519
	that's like so degenerate that it is not

	476
	00:17:02,240 --> 00:17:04,959
	hasn't learned anything useful about

	477
	00:17:03,519 --> 00:17:06,839
	your task at all so I think it might

	478
	00:17:04,959 --> 00:17:09,600
	depend on what task you're looking

	479
	00:17:06,839 --> 00:17:12,919
	at

	480
	00:17:09,600 --> 00:17:14,559
	yes this is for inference um so actually

	481
	00:17:12,919 --> 00:17:17,640
	everything we look at today will not

	482
	00:17:14,559 --> 00:17:17,640
	require aning of the

	483
	00:17:19,360 --> 00:17:26,559
	model Okay cool so now we're going to

	484
	00:17:24,000 --> 00:17:30,039
	step into sort of a slightly different

	485
	00:17:26,559 --> 00:17:31,280
	um set of strategies here which is maybe

	486
	00:17:30,039 --> 00:17:33,039
	we don't just want something from the

	487
	00:17:31,280 --> 00:17:35,160
	model distribution or something from a

	488
	00:17:33,039 --> 00:17:37,760
	modified distribution maybe we actually

	489
	00:17:35,160 --> 00:17:39,840
	just want the quote unquote best thing

	490
	00:17:37,760 --> 00:17:42,960
	the single most likely output given our

	491
	00:17:39,840 --> 00:17:45,200
	input right and here this would be the Y

	492
	00:17:42,960 --> 00:17:48,039
	hat the single sequence that satisfies

	493
	00:17:45,200 --> 00:17:51,919
	that has the highest score py given X

	494
	00:17:48,039 --> 00:17:54,240
	for the X that we gave the model um this

	495
	00:17:51,919 --> 00:17:56,000
	is this section is called mode seeking

	496
	00:17:54,240 --> 00:17:58,039
	search because this is the mode of the

	497
	00:17:56,000 --> 00:18:00,440
	distribution over outputs if you sampled

	498
	00:17:58,039 --> 00:18:01,760
	a huge huge number of times and you

	499
	00:18:00,440 --> 00:18:04,720
	looked at the single most likely

	500
	00:18:01,760 --> 00:18:06,720
	sequence you got it would be this y hat

	501
	00:18:04,720 --> 00:18:09,280
	and so how do we find this

	502
	00:18:06,720 --> 00:18:11,600
	thing well one idea is we know the

	503
	00:18:09,280 --> 00:18:13,159
	distribution at each individual setep

	504
	00:18:11,600 --> 00:18:16,000
	can we just pick the most likely thing

	505
	00:18:13,159 --> 00:18:18,960
	from that distribution and so in Greedy

	506
	00:18:16,000 --> 00:18:21,080
	decoding we take the argmax the single

	507
	00:18:18,960 --> 00:18:22,720
	highest probability token at each step

	508
	00:18:21,080 --> 00:18:24,840
	and we continue generating until the

	509
	00:18:22,720 --> 00:18:26,600
	single highest most the single highest

	510
	00:18:24,840 --> 00:18:28,840
	probability token is the stop token

	511
	00:18:26,600 --> 00:18:31,559
	right the end of sequence token

	512
	00:18:28,840 --> 00:18:33,400
	um for an individual token right if we

	513
	00:18:31,559 --> 00:18:35,559
	only want a single token output this is

	514
	00:18:33,400 --> 00:18:38,320
	exactly what we want this is the single

	515
	00:18:35,559 --> 00:18:40,400
	most likely output um and that's great

	516
	00:18:38,320 --> 00:18:44,000
	but if we're looking at something that

	517
	00:18:40,400 --> 00:18:45,120
	is maybe several tokens long are we

	518
	00:18:44,000 --> 00:18:47,360
	actually going to get the highest

	519
	00:18:45,120 --> 00:18:49,720
	probability thing and if you kind of

	520
	00:18:47,360 --> 00:18:52,159
	squint at this you can see that maybe we

	521
	00:18:49,720 --> 00:18:54,120
	have a problem here where the highest

	522
	00:18:52,159 --> 00:18:56,320
	probability sequence that you get from

	523
	00:18:54,120 --> 00:18:58,039
	multiplying across multiple steps

	524
	00:18:56,320 --> 00:18:59,559
	doesn't necessarily start with the token

	525
	00:18:58,039 --> 00:19:01,600
	that was highest probability at time

	526
	00:18:59,559 --> 00:19:03,200
	step one right maybe if you're doing

	527
	00:19:01,600 --> 00:19:04,720
	something like unconditional generation

	528
	00:19:03,200 --> 00:19:06,720
	the highest probability token at time

	529
	00:19:04,720 --> 00:19:08,360
	step one is always the but there could

	530
	00:19:06,720 --> 00:19:09,919
	be a really probable sentence that just

	531
	00:19:08,360 --> 00:19:11,480
	doesn't happen to start with the the

	532
	00:19:09,919 --> 00:19:12,720
	word the' and you would never find it

	533
	00:19:11,480 --> 00:19:15,080
	using GRE

	534
	00:19:12,720 --> 00:19:17,360
	decoding so this isn't going to give us

	535
	00:19:15,080 --> 00:19:19,799
	the highest probability output over a

	536
	00:19:17,360 --> 00:19:22,000
	sequence that's more than one token one

	537
	00:19:19,799 --> 00:19:23,360
	can we do anything better to try to find

	538
	00:19:22,000 --> 00:19:25,640
	this um

	539
	00:19:23,360 --> 00:19:27,559
	output and here we get into sort of one

	540
	00:19:25,640 --> 00:19:29,520
	of the most popular decoding methods the

	541
	00:19:27,559 --> 00:19:32,600
	one that you maybe heard of before which

	542
	00:19:29,520 --> 00:19:35,080
	is beam search the idea here is that we

	543
	00:19:32,600 --> 00:19:36,559
	don't want to miss a high probability

	544
	00:19:35,080 --> 00:19:38,880
	token that's hidden behind a lower

	545
	00:19:36,559 --> 00:19:40,200
	probability prefix so we want to kind of

	546
	00:19:38,880 --> 00:19:42,000
	search through a couple of different

	547
	00:19:40,200 --> 00:19:43,760
	options so that we don't discard

	548
	00:19:42,000 --> 00:19:47,120
	something too early that might have high

	549
	00:19:43,760 --> 00:19:49,360
	probability um later on in generation

	550
	00:19:47,120 --> 00:19:50,919
	and this is a type of bread first search

	551
	00:19:49,360 --> 00:19:53,200
	so we're going to look at a wide variety

	552
	00:19:50,919 --> 00:19:54,600
	of options at a given time step we're

	553
	00:19:53,200 --> 00:19:55,600
	going to pick some set of them to

	554
	00:19:54,600 --> 00:19:57,120
	continue and then we're going to look at

	555
	00:19:55,600 --> 00:19:58,919
	a wide variety of options for the next

	556
	00:19:57,120 --> 00:19:59,960
	time step instead of generating all the

	557
	00:19:58,919 --> 00:20:02,200
	way through a sequence and then

	558
	00:19:59,960 --> 00:20:04,320
	generating all the way through another

	559
	00:20:02,200 --> 00:20:05,760
	sequence um and how this works is we're

	560
	00:20:04,320 --> 00:20:07,559
	going to pick sort of a number of

	561
	00:20:05,760 --> 00:20:09,400
	candidates we'd like to explore a beam

	562
	00:20:07,559 --> 00:20:11,039
	with so in this example we're going to

	563
	00:20:09,400 --> 00:20:12,799
	pick three and we're going to say all

	564
	00:20:11,039 --> 00:20:15,480
	right here are maybe three options for

	565
	00:20:12,799 --> 00:20:17,640
	time step one for if we pick each of

	566
	00:20:15,480 --> 00:20:19,760
	those three options what would be the

	567
	00:20:17,640 --> 00:20:21,799
	three most likely things for time step

	568
	00:20:19,760 --> 00:20:23,200
	two right rather than choosing just the

	569
	00:20:21,799 --> 00:20:24,520
	single most likely thing in Greedy

	570
	00:20:23,200 --> 00:20:26,960
	decoding we're going to pick three

	571
	00:20:24,520 --> 00:20:29,120
	options and so now we have three options

	572
	00:20:26,960 --> 00:20:32,559
	for time step one three options for time

	573
	00:20:29,120 --> 00:20:34,280
	step two we now have nine options um

	574
	00:20:32,559 --> 00:20:36,320
	here right three options and then three

	575
	00:20:34,280 --> 00:20:37,679
	more for each of these and we don't want

	576
	00:20:36,320 --> 00:20:40,159
	to continue doing this because this is

	577
	00:20:37,679 --> 00:20:41,960
	going to sort of combinator explode so

	578
	00:20:40,159 --> 00:20:44,080
	we need to choose some subset of these

	579
	00:20:41,960 --> 00:20:45,880
	to continue with and the way we do that

	580
	00:20:44,080 --> 00:20:47,799
	is we look at the probability over this

	581
	00:20:45,880 --> 00:20:49,240
	two token sequence and we choose the two

	582
	00:20:47,799 --> 00:20:51,520
	that have the highest probability

	583
	00:20:49,240 --> 00:20:53,400
	overall so in this instance we've chosen

	584
	00:20:51,520 --> 00:20:55,679
	sort of one thing from this first group

	585
	00:20:53,400 --> 00:20:57,760
	and two things from the second group and

	586
	00:20:55,679 --> 00:20:59,760
	now we're back down to three hypotheses

	587
	00:20:57,760 --> 00:21:02,120
	each now two tokens long and we'll

	588
	00:20:59,760 --> 00:21:04,000
	continue generating to time step three

	589
	00:21:02,120 --> 00:21:05,600
	we'll get nine options we'll pre it back

	590
	00:21:04,000 --> 00:21:07,760
	down to three and we'll continue until

	591
	00:21:05,600 --> 00:21:09,159
	the end of generation where we now have

	592
	00:21:07,760 --> 00:21:10,679
	three sequences and we'll just pick the

	593
	00:21:09,159 --> 00:21:14,000
	one that's highest probability out of

	594
	00:21:10,679 --> 00:21:15,679
	those three to return um this is not

	595
	00:21:14,000 --> 00:21:17,360
	guaranteed to get you the highest

	596
	00:21:15,679 --> 00:21:18,480
	probability thing right you still have

	597
	00:21:17,360 --> 00:21:20,039
	this risk that you could be sort of

	598
	00:21:18,480 --> 00:21:22,279
	pruning out something that's high

	599
	00:21:20,039 --> 00:21:24,159
	probability but in general this sort of

	600
	00:21:22,279 --> 00:21:26,600
	works um much better than greedy

	601
	00:21:24,159 --> 00:21:28,520
	decoding and this is if you have a

	602
	00:21:26,600 --> 00:21:31,120
	language model and you're sort of not

	603
	00:21:28,520 --> 00:21:32,440
	what um decoding method it's using outs

	604
	00:21:31,120 --> 00:21:34,200
	are pretty good it's either beam search

	605
	00:21:32,440 --> 00:21:37,120
	or temperature samping right this is

	606
	00:21:34,200 --> 00:21:40,039
	very effective this is used um pretty

	607
	00:21:37,120 --> 00:21:41,760
	broadly there are however some issues

	608
	00:21:40,039 --> 00:21:43,760
	with beam search and one of the biggest

	609
	00:21:41,760 --> 00:21:46,159
	ones is that when you're doing this

	610
	00:21:43,760 --> 00:21:47,679
	maximum likelihood sampling you really

	611
	00:21:46,159 --> 00:21:50,080
	or the sampling to search for something

	612
	00:21:47,679 --> 00:21:51,760
	that's very high likelihood um you

	613
	00:21:50,080 --> 00:21:53,679
	really sacrifice a lot of diversity in

	614
	00:21:51,760 --> 00:21:55,320
	your outputs and in particular you could

	615
	00:21:53,679 --> 00:21:57,279
	wind up at the end of beam search with

	616
	00:21:55,320 --> 00:21:58,919
	three different outputs to choose from

	617
	00:21:57,279 --> 00:22:00,120
	that are all pretty pretty much the same

	618
	00:21:58,919 --> 00:22:02,640
	like they're slightly different token

	619
	00:22:00,120 --> 00:22:04,559
	sequences but they look very similar and

	620
	00:22:02,640 --> 00:22:07,480
	so maybe you want to S get sort of a

	621
	00:22:04,559 --> 00:22:08,919
	more diverse set um there's a couple of

	622
	00:22:07,480 --> 00:22:10,640
	different methods in this category I'm

	623
	00:22:08,919 --> 00:22:12,679
	going to very briefly shout out two of

	624
	00:22:10,640 --> 00:22:14,200
	them um but the idea here is to sort of

	625
	00:22:12,679 --> 00:22:16,440
	reintroduce some of the benefits of

	626
	00:22:14,200 --> 00:22:19,120
	sampling while still doing this kind of

	627
	00:22:16,440 --> 00:22:20,919
	search for high probability things um

	628
	00:22:19,120 --> 00:22:22,600
	diverse beam search is one of these

	629
	00:22:20,919 --> 00:22:25,520
	methods and here the idea is that we

	630
	00:22:22,600 --> 00:22:27,279
	want to modify that scoring step when we

	631
	00:22:25,520 --> 00:22:28,600
	choose which three out of our nine beams

	632
	00:22:27,279 --> 00:22:30,200
	we want to continue

	633
	00:22:28,600 --> 00:22:32,000
	to avoid choosing things that are really

	634
	00:22:30,200 --> 00:22:34,320
	really close to each other right so

	635
	00:22:32,000 --> 00:22:36,039
	maybe our highest probability thing is

	636
	00:22:34,320 --> 00:22:37,559
	some sequence a and then if we look at

	637
	00:22:36,039 --> 00:22:39,520
	the other sequences there's one that's

	638
	00:22:37,559 --> 00:22:41,279
	pretty high probability but very similar

	639
	00:22:39,520 --> 00:22:43,600
	to that sequence and there's one that's

	640
	00:22:41,279 --> 00:22:45,320
	like slightly lower probability but very

	641
	00:22:43,600 --> 00:22:47,200
	different and so maybe we would choose a

	642
	00:22:45,320 --> 00:22:49,679
	sequence that is a little lower

	643
	00:22:47,200 --> 00:22:51,760
	probability to maximize diversity in our

	644
	00:22:49,679 --> 00:22:53,799
	set to try to get like sort of a wider

	645
	00:22:51,760 --> 00:22:56,200
	range of options to choose from later in

	646
	00:22:53,799 --> 00:22:58,200
	generation so this modifies the scoring

	647
	00:22:56,200 --> 00:23:00,120
	to not just take into account likelihood

	648
	00:22:58,200 --> 00:23:03,200
	but also similarity to other

	649
	00:23:00,120 --> 00:23:05,400
	KS another option down this path is

	650
	00:23:03,200 --> 00:23:07,640
	stochastic beam search where we're going

	651
	00:23:05,400 --> 00:23:09,279
	to keep the scoring the same but rather

	652
	00:23:07,640 --> 00:23:11,679
	than choosing just the top three most

	653
	00:23:09,279 --> 00:23:13,279
	likely tokens to expand out each beam

	654
	00:23:11,679 --> 00:23:15,200
	we're actually going to sample from some

	655
	00:23:13,279 --> 00:23:17,000
	distribution and you could sample from

	656
	00:23:15,200 --> 00:23:18,760
	the model distribution directly using

	657
	00:23:17,000 --> 00:23:20,200
	ancestral sampling or you could use any

	658
	00:23:18,760 --> 00:23:22,679
	of our sampling methods we talked about

	659
	00:23:20,200 --> 00:23:24,200
	in the last section to do this and the

	660
	00:23:22,679 --> 00:23:25,799
	the idea here is sort of similar to

	661
	00:23:24,200 --> 00:23:29,279
	diverse beam search we want to get sort

	662
	00:23:25,799 --> 00:23:31,240
	of a wider exploration of our models

	663
	00:23:29,279 --> 00:23:33,520
	like output space you know we want to

	664
	00:23:31,240 --> 00:23:35,360
	sort of explore more things instead of

	665
	00:23:33,520 --> 00:23:36,760
	just seeking winding up with a bunch of

	666
	00:23:35,360 --> 00:23:39,679
	outputs that look very similar at the

	667
	00:23:36,760 --> 00:23:41,120
	end of beam search um if folks are

	668
	00:23:39,679 --> 00:23:43,679
	interested in these I think these are

	669
	00:23:41,120 --> 00:23:46,159
	both linked on the website um the the

	670
	00:23:43,679 --> 00:23:48,679
	papers that both of these ideas came

	671
	00:23:46,159 --> 00:23:51,480
	from

	672
	00:23:48,679 --> 00:23:54,400
	Yes um for stochastic

	673
	00:23:51,480 --> 00:23:57,039
	resarch the sampl probability takes into

	674
	00:23:54,400 --> 00:23:59,039
	account the current part that we already

	675
	00:23:57,039 --> 00:24:02,000
	travel okay

	676
	00:23:59,039 --> 00:24:04,320
	yeah exactly so it's this um like

	677
	00:24:02,000 --> 00:24:05,640
	selection step here but we're instead of

	678
	00:24:04,320 --> 00:24:07,760
	just doing greedy selection we're going

	679
	00:24:05,640 --> 00:24:11,760
	to do

	680
	00:24:07,760 --> 00:24:17,520
	assembling yes my question was on the T

	681
	00:24:11,760 --> 00:24:23,200
	yeah like you for something super simple

	682
	00:24:17,520 --> 00:24:26,520
	like if both of them have a high are you

	683
	00:24:23,200 --> 00:24:28,120
	like yeah so you would if it has a

	684
	00:24:26,520 --> 00:24:30,080
	really high probability under both

	685
	00:24:28,120 --> 00:24:32,880
	models it would have a lower probability

	686
	00:24:30,080 --> 00:24:35,080
	after doing this sort of contrasted

	687
	00:24:32,880 --> 00:24:36,600
	de right so if the if the smaller

	688
	00:24:35,080 --> 00:24:38,799
	model's really good at your task this

	689
	00:24:36,600 --> 00:24:40,960
	might not work very

	690
	00:24:38,799 --> 00:24:43,360
	well yeah I think in the paper they're

	691
	00:24:40,960 --> 00:24:45,320
	generally evaluating on these sort of

	692
	00:24:43,360 --> 00:24:48,279
	like open ended generation task I bet

	693
	00:24:45,320 --> 00:24:51,279
	this works a lot worse for

	694
	00:24:48,279 --> 00:24:51,279
	now

	695
	00:24:56,760 --> 00:24:59,760
	yes

	696
	00:25:02,440 --> 00:25:08,120
	you yeah this is a great question um and

	697
	00:25:05,960 --> 00:25:11,559
	so the question is how do we measure

	698
	00:25:08,120 --> 00:25:14,120
	similar beams um you can sort of Define

	699
	00:25:11,559 --> 00:25:15,559
	any kind of similarity function you like

	700
	00:25:14,120 --> 00:25:17,520
	here um anything that you'd use to

	701
	00:25:15,559 --> 00:25:20,440
	evaluate like how similar something is

	702
	00:25:17,520 --> 00:25:22,360
	to a gold reference right um I think in

	703
	00:25:20,440 --> 00:25:25,039
	the original diverse beam search they do

	704
	00:25:22,360 --> 00:25:27,760
	this by looking at like exact token

	705
	00:25:25,039 --> 00:25:30,640
	match across the two right like if these

	706
	00:25:27,760 --> 00:25:33,880
	beams are the same in all but one of the

	707
	00:25:30,640 --> 00:25:35,600
	tokens or they have like you know 50% of

	708
	00:25:33,880 --> 00:25:37,120
	the tokens are shared across the beams

	709
	00:25:35,600 --> 00:25:38,559
	and maybe these are really similar and

	710
	00:25:37,120 --> 00:25:40,559
	they should try to choose two things

	711
	00:25:38,559 --> 00:25:42,600
	that are different um but you could swap

	712
	00:25:40,559 --> 00:25:46,200
	that out for any

	713
	00:25:42,600 --> 00:25:49,440
	metc yes so

	714
	00:25:46,200 --> 00:25:50,960
	the there's kind of like a that's Happ

	715
	00:25:49,440 --> 00:25:53,360
	at

	716
	00:25:50,960 --> 00:25:55,000
	every for the stochastic be search

	717
	00:25:53,360 --> 00:25:57,720
	there's like a shering what do you mean

	718
	00:25:55,000 --> 00:26:00,520
	by a shepher so it says modify the next

	719
	00:25:57,720 --> 00:26:03,000
	sech selection because they're like um

	720
	00:26:00,520 --> 00:26:06,919
	it is searching at a different space and

	721
	00:26:03,000 --> 00:26:09,679
	it's not searching within the same 3D

	722
	00:26:06,919 --> 00:26:14,080
	SE is it searching in a different space

	723
	00:26:09,679 --> 00:26:15,799
	yeah so it's um in the same probability

	724
	00:26:14,080 --> 00:26:18,399
	distribution but it'll see a different

	725
	00:26:15,799 --> 00:26:20,840
	part of the distribution so when you're

	726
	00:26:18,399 --> 00:26:22,640
	doing the grey search you'll only ever

	727
	00:26:20,840 --> 00:26:24,559
	look at the top three tokens in the next

	728
	00:26:22,640 --> 00:26:27,120
	token distribution because you're just

	729
	00:26:24,559 --> 00:26:29,840
	selecting like the maximums um but in

	730
	00:26:27,120 --> 00:26:31,360
	sampling you could you could get the

	731
	00:26:29,840 --> 00:26:32,880
	same tokens right if they're really high

	732
	00:26:31,360 --> 00:26:35,720
	likelihood but you could also sample

	733
	00:26:32,880 --> 00:26:38,399
	something that's further down in the

	734
	00:26:35,720 --> 00:26:42,760
	distribution yeah as a followup to that

	735
	00:26:38,399 --> 00:26:44,880
	like into uh our stamping we take into

	736
	00:26:42,760 --> 00:26:46,960
	account the probability of the prefix

	737
	00:26:44,880 --> 00:26:50,679
	like the current hypothesis right

	738
	00:26:46,960 --> 00:26:51,760
	because otherwise it is the same as just

	739
	00:26:50,679 --> 00:26:54,279
	uh

	740
	00:26:51,760 --> 00:26:57,159
	in yeah so in the sampling we're taking

	741
	00:26:54,279 --> 00:27:00,120
	into account the previous the prefix

	742
	00:26:57,159 --> 00:27:02,600
	yeah so so it we will take into account

	743
	00:27:00,120 --> 00:27:06,200
	the prefix but this sampling mechanism

	744
	00:27:02,600 --> 00:27:08,320
	here could be ancestral sampling um the

	745
	00:27:06,200 --> 00:27:10,480
	only the difference here is that we're

	746
	00:27:08,320 --> 00:27:12,600
	also doing a sort of search step on top

	747
	00:27:10,480 --> 00:27:14,679
	of that to choose the maximum likelihood

	748
	00:27:12,600 --> 00:27:18,080
	things across multiple

	749
	00:27:14,679 --> 00:27:20,559
	me another important thing um is you

	750
	00:27:18,080 --> 00:27:22,279
	sample without replacement and so

	751
	00:27:20,559 --> 00:27:24,120
	normally you sample with replacement and

	752
	00:27:22,279 --> 00:27:25,840
	you might get exactly the same thing but

	753
	00:27:24,120 --> 00:27:28,000
	when you're doing stasic beam search you

	754
	00:27:25,840 --> 00:27:30,240
	sample without replacement so you get

	755
	00:27:28,000 --> 00:27:33,279
	like three ones according to the

	756
	00:27:30,240 --> 00:27:36,080
	probability but they're guaranteed to be

	757
	00:27:33,279 --> 00:27:37,799
	different right so beam search like one

	758
	00:27:36,080 --> 00:27:39,559
	of the characteristics of beam search is

	759
	00:27:37,799 --> 00:27:41,640
	you always get three different things

	760
	00:27:39,559 --> 00:27:44,240
	because you're picking the three top

	761
	00:27:41,640 --> 00:27:45,760
	when you do sampling uh like stochastic

	762
	00:27:44,240 --> 00:27:47,399
	Bean shirts you get three different

	763
	00:27:45,760 --> 00:27:49,440
	things they're not guaranteed to be the

	764
	00:27:47,399 --> 00:27:51,760
	top they could be distributed according

	765
	00:27:49,440 --> 00:27:54,360
	to the prob distribution but they're

	766
	00:27:51,760 --> 00:27:55,840
	guaranteed so um you can take a look at

	767
	00:27:54,360 --> 00:27:58,039
	the paper for more details of exactly

	768
	00:27:55,840 --> 00:28:00,159
	how it looks but that that's

	769
	00:27:58,039 --> 00:28:03,039
	so then is the main difference that

	770
	00:28:00,159 --> 00:28:05,120
	compared to plus temping that we have n

	771
	00:28:03,039 --> 00:28:08,519
	options that we're cheing tet instead of

	772
	00:28:05,120 --> 00:28:10,320
	going with the going with only one and

	773
	00:28:08,519 --> 00:28:11,200
	you can't yeah you can't simple the same

	774
	00:28:10,320 --> 00:28:14,960
	thing

	775
	00:28:11,200 --> 00:28:16,919
	right yeah so just uh repeat recording

	776
	00:28:14,960 --> 00:28:19,159
	is that n options we're keeping track of

	777
	00:28:16,919 --> 00:28:22,240
	and they're all going to be unique token

	778
	00:28:19,159 --> 00:28:24,240
	sequences at least um you can actually

	779
	00:28:22,240 --> 00:28:26,200
	get the same output sequence from two

	780
	00:28:24,240 --> 00:28:28,120
	different toen sequences if you tokenize

	781
	00:28:26,200 --> 00:28:32,360
	slightly differently um but these will

	782
	00:28:28,120 --> 00:28:37,840
	always be unique tokens

	783
	00:28:32,360 --> 00:28:39,279
	Le so that was sort of a a why like a a

	784
	00:28:37,840 --> 00:28:41,320
	set of methods that we've developed to

	785
	00:28:39,279 --> 00:28:43,600
	try to find the most probable sequence

	786
	00:28:41,320 --> 00:28:44,480
	out of the model um but in the next

	787
	00:28:43,600 --> 00:28:46,039
	section here we're going to sort of

	788
	00:28:44,480 --> 00:28:50,240
	think about whether that's actually what

	789
	00:28:46,039 --> 00:28:51,679
	we want to do at all um so what is like

	790
	00:28:50,240 --> 00:28:54,240
	is do we really want the highest

	791
	00:28:51,679 --> 00:28:56,880
	probability thing um we know that

	792
	00:28:54,240 --> 00:28:58,600
	outputs with really low probability tend

	793
	00:28:56,880 --> 00:29:00,640
	to be really like worse than outfits

	794
	00:28:58,600 --> 00:29:03,240
	with high probability right maybe I'm

	795
	00:29:00,640 --> 00:29:05,840
	trying to predict like what the next

	796
	00:29:03,240 --> 00:29:08,640
	sentence should be after the cat saw the

	797
	00:29:05,840 --> 00:29:11,240
	dog right the cat sat down is way higher

	798
	00:29:08,640 --> 00:29:12,559
	probability than the cat grew wings and

	799
	00:29:11,240 --> 00:29:14,039
	at least with the cats I've met that

	800
	00:29:12,559 --> 00:29:15,679
	sounds pretty that sounds pretty much

	801
	00:29:14,039 --> 00:29:19,559
	right right like this is a much better

	802
	00:29:15,679 --> 00:29:21,720
	output than the cat gr wings but if you

	803
	00:29:19,559 --> 00:29:24,159
	look at just the outputs with relatively

	804
	00:29:21,720 --> 00:29:25,960
	high probability it's sort of less clear

	805
	00:29:24,159 --> 00:29:27,880
	that this defines an exact ranking

	806
	00:29:25,960 --> 00:29:30,559
	between those outputs right

	807
	00:29:27,880 --> 00:29:32,600
	is the cat sat down necessarily better

	808
	00:29:30,559 --> 00:29:34,519
	than the cat ran away these both seem

	809
	00:29:32,600 --> 00:29:35,720
	like pretty reasonable outputs to me

	810
	00:29:34,519 --> 00:29:40,200
	even though one of them is slightly

	811
	00:29:35,720 --> 00:29:42,799
	higher probability and so we do we

	812
	00:29:40,200 --> 00:29:45,240
	really like necessarily need to recover

	813
	00:29:42,799 --> 00:29:47,200
	the cat that down um and this gets a

	814
	00:29:45,240 --> 00:29:49,399
	little a little more complicated still

	815
	00:29:47,200 --> 00:29:51,120
	if we look at sort of a range of outputs

	816
	00:29:49,399 --> 00:29:53,120
	so say there's sort of six outputs that

	817
	00:29:51,120 --> 00:29:55,240
	our model could give us um and here

	818
	00:29:53,120 --> 00:29:57,559
	we're looking at sort of full sequences

	819
	00:29:55,240 --> 00:30:00,120
	not individual tokens just for clarity

	820
	00:29:57,559 --> 00:30:02,640
	so maybe our outputs in order of

	821
	00:30:00,120 --> 00:30:05,840
	probability are the cat sat down it ran

	822
	00:30:02,640 --> 00:30:08,240
	away it sprinted off it got out of there

	823
	00:30:05,840 --> 00:30:09,720
	it's very small and it grew Wings right

	824
	00:30:08,240 --> 00:30:11,440
	so we're definitely sure that the cat

	825
	00:30:09,720 --> 00:30:13,159
	sat down is a better output than the cat

	826
	00:30:11,440 --> 00:30:15,360
	grew wings and if we're doing a mod

	827
	00:30:13,159 --> 00:30:17,600
	seeking search we would find that as our

	828
	00:30:15,360 --> 00:30:19,440
	most likely thing if we're if we you

	829
	00:30:17,600 --> 00:30:21,440
	know do a good job searching and we'd

	830
	00:30:19,440 --> 00:30:23,519
	return that as our output but if you

	831
	00:30:21,440 --> 00:30:25,919
	look at the rest of this distribution

	832
	00:30:23,519 --> 00:30:27,880
	you see that there's actually a whole

	833
	00:30:25,919 --> 00:30:29,240
	set of outputs after that all say

	834
	00:30:27,880 --> 00:30:31,720
	something that kind of means the cat

	835
	00:30:29,240 --> 00:30:33,480
	left the area right it's just that this

	836
	00:30:31,720 --> 00:30:35,200
	probability is split over these three

	837
	00:30:33,480 --> 00:30:37,080
	different generations and if you

	838
	00:30:35,200 --> 00:30:39,120
	actually add up the probability mass of

	839
	00:30:37,080 --> 00:30:40,880
	all three of these sequences this is

	840
	00:30:39,120 --> 00:30:42,919
	double the probability mass of the cat

	841
	00:30:40,880 --> 00:30:44,360
	sat down but because none of these

	842
	00:30:42,919 --> 00:30:45,960
	individual sequences is higher

	843
	00:30:44,360 --> 00:30:47,399
	probability if you're doing mode seeking

	844
	00:30:45,960 --> 00:30:50,640
	search you wouldn't you wouldn't be able

	845
	00:30:47,399 --> 00:30:52,480
	to see this effect right so do we really

	846
	00:30:50,640 --> 00:30:53,760
	want to return the cat sat down or do we

	847
	00:30:52,480 --> 00:30:55,200
	want to return something that means the

	848
	00:30:53,760 --> 00:30:57,559
	cat left the

	849
	00:30:55,200 --> 00:30:59,200
	area the question then is like if it's

	850
	00:30:57,559 --> 00:31:03,120
	not probability that makes an output

	851
	00:30:59,200 --> 00:31:04,679
	good what is it so we have this one

	852
	00:31:03,120 --> 00:31:06,039
	output that's really high probability

	853
	00:31:04,679 --> 00:31:09,000
	but it's very different from everything

	854
	00:31:06,039 --> 00:31:10,720
	else in our set and then we have a

	855
	00:31:09,000 --> 00:31:13,200
	couple of outputs that are all pretty

	856
	00:31:10,720 --> 00:31:15,080
	high probability and similar to a bunch

	857
	00:31:13,200 --> 00:31:17,840
	of other relatively high probability

	858
	00:31:15,080 --> 00:31:19,720
	things so maybe it's sort of less risky

	859
	00:31:17,840 --> 00:31:21,399
	to return one of these right are thing

	860
	00:31:19,720 --> 00:31:23,200
	that's higher probability but different

	861
	00:31:21,399 --> 00:31:24,600
	than everything else could be different

	862
	00:31:23,200 --> 00:31:26,840
	because it's way better or it could be

	863
	00:31:24,600 --> 00:31:29,000
	different because it's way worse um

	864
	00:31:26,840 --> 00:31:31,120
	another way to think about this is you

	865
	00:31:29,000 --> 00:31:32,600
	know maybe if you and your friends were

	866
	00:31:31,120 --> 00:31:34,200
	cheating on a test which you shouldn't

	867
	00:31:32,600 --> 00:31:35,480
	do but if you were going to do it and

	868
	00:31:34,200 --> 00:31:37,519
	all of your friends sent you their

	869
	00:31:35,480 --> 00:31:39,240
	answers um maybe one of your friends has

	870
	00:31:37,519 --> 00:31:40,960
	a slightly higher score in the class

	871
	00:31:39,240 --> 00:31:42,519
	than everyone else but they said the

	872
	00:31:40,960 --> 00:31:44,480
	answer was answer a and everyone else

	873
	00:31:42,519 --> 00:31:45,799
	said the answer was B right you still

	874
	00:31:44,480 --> 00:31:48,480
	might go with the answer that everyone

	875
	00:31:45,799 --> 00:31:50,679
	else said because like what there's it

	876
	00:31:48,480 --> 00:31:52,679
	sort of feels less risky like maybe

	877
	00:31:50,679 --> 00:31:54,440
	everyone else got the answer get that

	878
	00:31:52,679 --> 00:31:55,880
	answer and so your one friend could be

	879
	00:31:54,440 --> 00:31:56,919
	right when everyone else is wrong or

	880
	00:31:55,880 --> 00:31:59,679
	they could have made a mistake that no

	881
	00:31:56,919 --> 00:32:01,240
	one El else is making so this is sort of

	882
	00:31:59,679 --> 00:32:03,519
	the same concept right we want an output

	883
	00:32:01,240 --> 00:32:06,320
	that's relatively high probability but

	884
	00:32:03,519 --> 00:32:09,399
	also relatively low

	885
	00:32:06,320 --> 00:32:11,320
	risk and so here maybe if we were using

	886
	00:32:09,399 --> 00:32:13,679
	this criteria we'd return the cat ran

	887
	00:32:11,320 --> 00:32:14,720
	away as our sort of as our sort of

	888
	00:32:13,679 --> 00:32:16,720
	single

	889
	00:32:14,720 --> 00:32:19,440
	output so how do you find something

	890
	00:32:16,720 --> 00:32:21,000
	that's high probability and low risk

	891
	00:32:19,440 --> 00:32:22,480
	there's sort of two questions here right

	892
	00:32:21,000 --> 00:32:24,399
	we have to figure out how to estimate

	893
	00:32:22,480 --> 00:32:26,120
	probability and if we're looking at a

	894
	00:32:24,399 --> 00:32:28,519
	set of outputs like the six we saw

	895
	00:32:26,120 --> 00:32:29,880
	before maybe we can just do this by

	896
	00:32:28,519 --> 00:32:31,720
	counting right we could sample

	897
	00:32:29,880 --> 00:32:34,000
	everything from the model and get exact

	898
	00:32:31,720 --> 00:32:35,200
	probability or we could take a sample

	899
	00:32:34,000 --> 00:32:38,080
	from the model and just look at

	900
	00:32:35,200 --> 00:32:40,200
	probabilities in that set and from there

	901
	00:32:38,080 --> 00:32:41,840
	from that sample um sort of one

	902
	00:32:40,200 --> 00:32:43,559
	reasonable thing to do is just count

	903
	00:32:41,840 --> 00:32:45,320
	frequency right if something's in our

	904
	00:32:43,559 --> 00:32:47,919
	sample twice as often we just say it's

	905
	00:32:45,320 --> 00:32:49,799
	twice as frequent or it's twice as

	906
	00:32:47,919 --> 00:32:52,880
	probable um this is something called

	907
	00:32:49,799 --> 00:32:54,440
	Monte Carlos sampling if you do this um

	908
	00:32:52,880 --> 00:32:56,039
	enough times like if you sample an

	909
	00:32:54,440 --> 00:32:58,279
	infinite set this is would give you

	910
	00:32:56,039 --> 00:33:00,880
	exactly the model distri distribution um

	911
	00:32:58,279 --> 00:33:02,840
	but for the sort of reasonable size sets

	912
	00:33:00,880 --> 00:33:04,200
	we're working with maybe like a 100

	913
	00:33:02,840 --> 00:33:06,320
	samples this gives us a sort of

	914
	00:33:04,200 --> 00:33:09,440
	reasonable approximation for what we for

	915
	00:33:06,320 --> 00:33:10,840
	what we need to do here at least so

	916
	00:33:09,440 --> 00:33:12,000
	we're just going to take a sample to get

	917
	00:33:10,840 --> 00:33:13,440
	probability and we're just going to

	918
	00:33:12,000 --> 00:33:15,519
	count things in that sample to see how

	919
	00:33:13,440 --> 00:33:17,320
	likely things are that doesn't seem too

	920
	00:33:15,519 --> 00:33:20,080
	bad how do we estimate

	921
	00:33:17,320 --> 00:33:21,679
	risk the idea here is that we have a

	922
	00:33:20,080 --> 00:33:24,080
	bunch of other things in this set of

	923
	00:33:21,679 --> 00:33:26,080
	outputs and we can treat those as sort

	924
	00:33:24,080 --> 00:33:27,880
	of like pseudo references right we can

	925
	00:33:26,080 --> 00:33:29,840
	evaluate agreement between the thing

	926
	00:33:27,880 --> 00:33:31,519
	we're looking at and each of those other

	927
	00:33:29,840 --> 00:33:33,480
	references and this is sort of the same

	928
	00:33:31,519 --> 00:33:35,519
	idea of calculating similarity in

	929
	00:33:33,480 --> 00:33:37,159
	diverse beam search we're going to use

	930
	00:33:35,519 --> 00:33:39,639
	some kind of metric to compare how

	931
	00:33:37,159 --> 00:33:41,279
	similar these things are um this metric

	932
	00:33:39,639 --> 00:33:43,080
	could be anything you use Downstream it

	933
	00:33:41,279 --> 00:33:44,840
	could be like an engram overlap metric

	934
	00:33:43,080 --> 00:33:48,600
	like Rouge or blue or it could also be

	935
	00:33:44,840 --> 00:33:51,120
	something um neural or semantic like um

	936
	00:33:48,600 --> 00:33:54,799
	something like BT score or Bart

	937
	00:33:51,120 --> 00:33:56,600
	score and so this concept um is a type

	938
	00:33:54,799 --> 00:33:57,919
	of decoding called minimum based risk

	939
	00:33:56,600 --> 00:33:59,600
	decoding

	940
	00:33:57,919 --> 00:34:01,840
	and what this equation captures is

	941
	00:33:59,600 --> 00:34:03,919
	exactly the intuition that we were um

	942
	00:34:01,840 --> 00:34:06,600
	sort of talking about just a slide ago

	943
	00:34:03,919 --> 00:34:08,159
	where we're going to choose something

	944
	00:34:06,600 --> 00:34:09,919
	that is low risk which means it's

	945
	00:34:08,159 --> 00:34:11,960
	similar to a lot of other things in this

	946
	00:34:09,919 --> 00:34:12,800
	set of outputs we've sampled and we're

	947
	00:34:11,960 --> 00:34:14,800
	going to choose something that's

	948
	00:34:12,800 --> 00:34:17,560
	relatively high probability which means

	949
	00:34:14,800 --> 00:34:19,159
	that sort of when we sum up over this if

	950
	00:34:17,560 --> 00:34:21,399
	something occurs in our set a bunch of

	951
	00:34:19,159 --> 00:34:23,320
	times it's going to have pretty strong

	952
	00:34:21,399 --> 00:34:25,800
	weight in picking which um of these

	953
	00:34:23,320 --> 00:34:27,000
	outputs are similar right if sort of

	954
	00:34:25,800 --> 00:34:28,399
	there's one thing in the set that

	955
	00:34:27,000 --> 00:34:29,919
	appears a bunch of times it's going to

	956
	00:34:28,399 --> 00:34:32,040
	have a strong influence on which thing

	957
	00:34:29,919 --> 00:34:34,119
	we pick and so that kind of captures

	958
	00:34:32,040 --> 00:34:38,520
	high probability in this

	959
	00:34:34,119 --> 00:34:41,119
	setting so to see how this works we can

	960
	00:34:38,520 --> 00:34:44,639
	look at an example um in

	961
	00:34:41,119 --> 00:34:47,399
	summarization so we choose some Metric

	962
	00:34:44,639 --> 00:34:49,639
	maybe we choose um Rouge which is an

	963
	00:34:47,399 --> 00:34:51,399
	engram overlap metric for summarization

	964
	00:34:49,639 --> 00:34:52,879
	and we say we're going to sample 100

	965
	00:34:51,399 --> 00:34:55,960
	things and we're going to use this

	966
	00:34:52,879 --> 00:35:00,359
	equation to choose the one that has the

	967
	00:34:55,960 --> 00:35:03,960
	sort of lower EST risk according to MBR

	968
	00:35:00,359 --> 00:35:06,480
	um so if we do that and we look at this

	969
	00:35:03,960 --> 00:35:07,560
	sort of table of results here um you can

	970
	00:35:06,480 --> 00:35:09,680
	see that this

	971
	00:35:07,560 --> 00:35:11,320
	outperforms the other sampling methods

	972
	00:35:09,680 --> 00:35:13,720
	that we've looked at before so greedy

	973
	00:35:11,320 --> 00:35:15,640
	decoding here is just sampling the

	974
	00:35:13,720 --> 00:35:18,760
	single most likely thing in each step

	975
	00:35:15,640 --> 00:35:21,800
	beam search here is the BS with five or

	976
	00:35:18,760 --> 00:35:24,359
	10 beams and DBS is the diverse beam

	977
	00:35:21,800 --> 00:35:27,040
	search we were talking about um if we

	978
	00:35:24,359 --> 00:35:29,440
	use minimum based risk and we use grou

	979
	00:35:27,040 --> 00:35:31,240
	is the sort of determiner of similarity

	980
	00:35:29,440 --> 00:35:32,680
	we do way better across all of our

	981
	00:35:31,240 --> 00:35:33,960
	metrics but we especially do really good

	982
	00:35:32,680 --> 00:35:36,680
	at Rouge because that's sort of the

	983
	00:35:33,960 --> 00:35:38,119
	metric that we've been using to evaluate

	984
	00:35:36,680 --> 00:35:40,240
	and then if we swap this out for other

	985
	00:35:38,119 --> 00:35:43,599
	metrics you still see an performance

	986
	00:35:40,240 --> 00:35:46,440
	improvement over these um search methods

	987
	00:35:43,599 --> 00:35:48,119
	here um what's the sort of catch here

	988
	00:35:46,440 --> 00:35:49,920
	the catch here is that MBR requires you

	989
	00:35:48,119 --> 00:35:51,599
	to sample a hundred things and so this

	990
	00:35:49,920 --> 00:35:54,760
	is a lot more expensive it's a lot

	991
	00:35:51,599 --> 00:35:54,760
	slower at infin

	992
	00:35:54,800 --> 00:35:58,800
	time um yes

	993
	00:36:04,200 --> 00:36:10,040
	yes a great question why does the beam

	994
	00:36:07,000 --> 00:36:14,000
	search with more beams perform worse um

	995
	00:36:10,040 --> 00:36:16,720
	this is a well a relatively welln

	996
	00:36:14,000 --> 00:36:19,359
	phenomena called the cursive beam search

	997
	00:36:16,720 --> 00:36:21,640
	which is we actually lost your M so you

	998
	00:36:19,359 --> 00:36:24,599
	mic and we can speak okay yeah so this

	999
	00:36:21,640 --> 00:36:26,079
	is called the cursive beam search um and

	1000
	00:36:24,599 --> 00:36:27,760
	the idea here is that beam search is

	1001
	00:36:26,079 --> 00:36:29,359
	like an approxim search right so if you

	1002
	00:36:27,760 --> 00:36:31,200
	add more beams you should be doing

	1003
	00:36:29,359 --> 00:36:33,319
	better and better at finding the maximum

	1004
	00:36:31,200 --> 00:36:34,800
	likelihood thing and generally you are

	1005
	00:36:33,319 --> 00:36:37,160
	you get something that is higher

	1006
	00:36:34,800 --> 00:36:39,160
	probability but as you add more beams

	1007
	00:36:37,160 --> 00:36:42,319
	you also often get something that does

	1008
	00:36:39,160 --> 00:36:42,319
	worse on your Downstream

	1009
	00:36:44,160 --> 00:36:47,560
	metrics back up

	1010
	00:36:54,240 --> 00:36:58,680
	there is that back online

	1011
	00:36:59,119 --> 00:37:06,520
	yeah is that back is that any louder no

	1012
	00:37:03,520 --> 00:37:06,520
	it

	1013
	00:37:07,000 --> 00:37:12,640
	question oh there we go is that better

	1014
	00:37:09,599 --> 00:37:13,760
	great um yeah so why why does this

	1015
	00:37:12,640 --> 00:37:16,040
	happen right why do you get something

	1016
	00:37:13,760 --> 00:37:18,560
	that's higher likelihood but um lower

	1017
	00:37:16,040 --> 00:37:22,040
	performance Downstream um and this is

	1018
	00:37:18,560 --> 00:37:24,000
	like another sort of degeneracy of beam

	1019
	00:37:22,040 --> 00:37:25,680
	search that this idea that the thing

	1020
	00:37:24,000 --> 00:37:27,440
	that is the absolute highest likelihood

	1021
	00:37:25,680 --> 00:37:28,599
	in your distribution might not actually

	1022
	00:37:27,440 --> 00:37:31,079
	be what you want

	1023
	00:37:28,599 --> 00:37:33,960
	Downstream um this is sort of one of the

	1024
	00:37:31,079 --> 00:37:35,200
	other things that people use to motivate

	1025
	00:37:33,960 --> 00:37:37,599
	why you might want to do something like

	1026
	00:37:35,200 --> 00:37:39,400
	MBR instead um and there's a great paper

	1027
	00:37:37,599 --> 00:37:41,640
	about this problem called the inadequacy

	1028
	00:37:39,400 --> 00:37:43,680
	of the mode because beam search is

	1029
	00:37:41,640 --> 00:37:45,520
	looking for the mode of the

	1030
	00:37:43,680 --> 00:37:47,880
	distribution well one other thing I'd

	1031
	00:37:45,520 --> 00:37:49,680
	like to mention is it also goes together

	1032
	00:37:47,880 --> 00:37:51,119
	with how you train your models because

	1033
	00:37:49,680 --> 00:37:53,760
	most of our models are trained using

	1034
	00:37:51,119 --> 00:37:57,079
	maximum likelihood maximum likelihood

	1035
	00:37:53,760 --> 00:37:59,040
	isn't explicitly maximizing our ability

	1036
	00:37:57,079 --> 00:38:01,079
	to get the best answer it's explicitly

	1037
	00:37:59,040 --> 00:38:05,720
	maximizing our ability to estimate the

	1038
	00:38:01,079 --> 00:38:10,160
	the distribution of answers so if I

	1039
	00:38:05,720 --> 00:38:13,040
	say um if you said like what is what is

	1040
	00:38:10,160 --> 00:38:15,839
	your favorite hobby or something like

	1041
	00:38:13,040 --> 00:38:17,680
	that uh what is your favorite hobby in a

	1042
	00:38:15,839 --> 00:38:19,280
	dialogue system often it'll answer I

	1043
	00:38:17,680 --> 00:38:22,400
	don't know or something like that

	1044
	00:38:19,280 --> 00:38:24,920
	because it like you know that that's

	1045
	00:38:22,400 --> 00:38:26,599
	more likely than answering any specific

	1046
	00:38:24,920 --> 00:38:29,240
	hobby like it's more likely than

	1047
	00:38:26,599 --> 00:38:32,119
	answering basketball bowling you know

	1048
	00:38:29,240 --> 00:38:35,040
	whatever else because you have many many

	1049
	00:38:32,119 --> 00:38:36,560
	different options and so like especially

	1050
	00:38:35,040 --> 00:38:39,880
	if it's something that's a little bit

	1051
	00:38:36,560 --> 00:38:42,160
	more comp complicated it will avoid

	1052
	00:38:39,880 --> 00:38:44,680
	answering that and in particular it ends

	1053
	00:38:42,160 --> 00:38:47,240
	up answering very short things for

	1054
	00:38:44,680 --> 00:38:49,280
	example um or sometimes it ends up

	1055
	00:38:47,240 --> 00:38:51,160
	repeating itself over and over again or

	1056
	00:38:49,280 --> 00:38:53,240
	or things like that so it also goes

	1057
	00:38:51,160 --> 00:38:57,760
	together with like the training of the

	1058
	00:38:53,240 --> 00:38:59,359
	model yeah and this is um one of the

	1059
	00:38:57,760 --> 00:39:01,079
	this is still a problem in modern

	1060
	00:38:59,359 --> 00:39:02,560
	systems so if you actually look at the

	1061
	00:39:01,079 --> 00:39:03,839
	single like if you could enumerate

	1062
	00:39:02,560 --> 00:39:05,680
	everything and see the single most

	1063
	00:39:03,839 --> 00:39:07,520
	likely sequence it's often the empty

	1064
	00:39:05,680 --> 00:39:10,920
	sequence just not opening anything at

	1065
	00:39:07,520 --> 00:39:12,640
	all um and so if that's your true mode

	1066
	00:39:10,920 --> 00:39:16,119
	of the distribution then doing better at

	1067
	00:39:12,640 --> 00:39:16,119
	mode seeking is not always like

	1068
	00:39:16,599 --> 00:39:19,599
	helpful

	1069
	00:39:25,440 --> 00:39:32,960
	yes could this be influenced by the

	1070
	00:39:28,200 --> 00:39:32,960
	confidence problem like um how

	1071
	00:39:37,560 --> 00:39:41,079
	so seems

	1072
	00:39:49,760 --> 00:39:53,599
	bees

	1073
	00:39:51,010 --> 00:39:57,280
	[Music]

	1074
	00:39:53,599 --> 00:39:59,760
	might right I think I I think I see

	1075
	00:39:57,280 --> 00:40:02,000
	what you're saying which is that like

	1076
	00:39:59,760 --> 00:40:04,200
	the the confidence gives you the

	1077
	00:40:02,000 --> 00:40:06,680
	confidence of like a single exact

	1078
	00:40:04,200 --> 00:40:11,000
	sequence right not the like actual sort

	1079
	00:40:06,680 --> 00:40:13,200
	of semantic space of and so yeah if you

	1080
	00:40:11,000 --> 00:40:14,920
	looked at just like the if you look at

	1081
	00:40:13,200 --> 00:40:17,000
	just the probability scores you get the

	1082
	00:40:14,920 --> 00:40:18,520
	probability of an exact string when what

	1083
	00:40:17,000 --> 00:40:20,119
	you really actually care about with

	1084
	00:40:18,520 --> 00:40:22,319
	confidence is the probability of sort of

	1085
	00:40:20,119 --> 00:40:23,800
	like things that mean the same thing

	1086
	00:40:22,319 --> 00:40:25,359
	yeah this is um part of why like

	1087
	00:40:23,800 --> 00:40:28,359
	calibration is really hard for long

	1088
	00:40:25,359 --> 00:40:28,359
	sequences

	1089
	00:40:30,720 --> 00:40:37,319
	great so we're g to touch sort of

	1090
	00:40:34,359 --> 00:40:39,520
	briefly on a couple of other things that

	1091
	00:40:37,319 --> 00:40:40,920
	aren't sort of always explicitly

	1092
	00:40:39,520 --> 00:40:42,480
	described in this framework but that you

	1093
	00:40:40,920 --> 00:40:45,040
	can think of as variance of minimum

	1094
	00:40:42,480 --> 00:40:46,960
	based risk um and if you're interested

	1095
	00:40:45,040 --> 00:40:49,560
	in this analysis um I think as Graham

	1096
	00:40:46,960 --> 00:40:51,800
	mentioned earlier um Alex Z is a first

	1097
	00:40:49,560 --> 00:40:53,680
	year MLT and I wrote a paper about this

	1098
	00:40:51,800 --> 00:40:57,839
	um which you can check out if you're

	1099
	00:40:53,680 --> 00:41:01,200
	interested so the um two that I really

	1100
	00:40:57,839 --> 00:41:03,800
	want to touch on here are other sort of

	1101
	00:41:01,200 --> 00:41:05,240
	inference time things you can consider

	1102
	00:41:03,800 --> 00:41:07,520
	which might look a little bit different

	1103
	00:41:05,240 --> 00:41:09,480
	on the first BL um the first of these is

	1104
	00:41:07,520 --> 00:41:11,680
	output ensembling so say you have

	1105
	00:41:09,480 --> 00:41:13,240
	multiple different models and you get

	1106
	00:41:11,680 --> 00:41:15,480
	outputs from all of them and now you

	1107
	00:41:13,240 --> 00:41:19,560
	need to choose a best output among that

	1108
	00:41:15,480 --> 00:41:21,599
	set um one of the sort of common ways to

	1109
	00:41:19,560 --> 00:41:24,480
	do this is to compare like an embedding

	1110
	00:41:21,599 --> 00:41:25,920
	similarity across models like does model

	1111
	00:41:24,480 --> 00:41:27,560
	one think these two things are really

	1112
	00:41:25,920 --> 00:41:28,880
	similar does model two think these two

	1113
	00:41:27,560 --> 00:41:32,599
	things are really similar and try to

	1114
	00:41:28,880 --> 00:41:34,680
	choose something that the um has really

	1115
	00:41:32,599 --> 00:41:37,319
	high similarity with a lot of other

	1116
	00:41:34,680 --> 00:41:39,200
	outputs um of course now that we've just

	1117
	00:41:37,319 --> 00:41:41,440
	recently been talking about MBR you can

	1118
	00:41:39,200 --> 00:41:44,920
	see that you can probably see that this

	1119
	00:41:41,440 --> 00:41:46,280
	is um the same general formulation just

	1120
	00:41:44,920 --> 00:41:47,880
	rather than summing over a set of

	1121
	00:41:46,280 --> 00:41:49,520
	outputs from a single model now you're

	1122
	00:41:47,880 --> 00:41:52,160
	looking at outputs over a whole set of

	1123
	00:41:49,520 --> 00:41:54,640
	models um so some types of ensembling

	1124
	00:41:52,160 --> 00:41:57,319
	fall into this category of minimum based

	1125
	00:41:54,640 --> 00:42:00,680
	risk methods another thing in this

	1126
	00:41:57,319 --> 00:42:03,280
	category is a um sort of recent decoding

	1127
	00:42:00,680 --> 00:42:06,079
	method called self-consistency and the

	1128
	00:42:03,280 --> 00:42:08,200
	idea here is that you want to do

	1129
	00:42:06,079 --> 00:42:09,359
	something like mathematical reasoning

	1130
	00:42:08,200 --> 00:42:10,599
	and you really care about getting the

	1131
	00:42:09,359 --> 00:42:12,000
	final answer right but you don't

	1132
	00:42:10,599 --> 00:42:15,000
	necessarily care about getting all of

	1133
	00:42:12,000 --> 00:42:18,079
	the the reasoning steps in between right

	1134
	00:42:15,000 --> 00:42:19,520
	so you prompt the model for an answer um

	1135
	00:42:18,079 --> 00:42:20,800
	using something like Chain of Thought

	1136
	00:42:19,520 --> 00:42:22,680
	right you ask it to sort of talk through

	1137
	00:42:20,800 --> 00:42:26,440
	the steps it's going to do and then give

	1138
	00:42:22,680 --> 00:42:28,599
	you a final answer um you sample many

	1139
	00:42:26,440 --> 00:42:30,400
	puts using this and then you completely

	1140
	00:42:28,599 --> 00:42:32,200
	throw away the chains of thought um and

	1141
	00:42:30,400 --> 00:42:35,359
	you just take the answer from each

	1142
	00:42:32,200 --> 00:42:37,640
	output um you have that set of answers

	1143
	00:42:35,359 --> 00:42:38,960
	maybe you have like 20 30 100 answers

	1144
	00:42:37,640 --> 00:42:40,000
	you just return the one that was most

	1145
	00:42:38,960 --> 00:42:43,720
	frequently

	1146
	00:42:40,000 --> 00:42:46,119
	generated um what this is doing is a

	1147
	00:42:43,720 --> 00:42:48,800
	type of MBR where the metric that you

	1148
	00:42:46,119 --> 00:42:51,160
	actually care about is exact match of

	1149
	00:42:48,800 --> 00:42:51,839
	this answer right ignoring the rest of

	1150
	00:42:51,160 --> 00:42:54,079
	the

	1151
	00:42:51,839 --> 00:42:55,800
	generation um and so here we have sort

	1152
	00:42:54,079 --> 00:42:56,839
	of the same intuition that we want an

	1153
	00:42:55,800 --> 00:42:59,160
	output

	1154
	00:42:56,839 --> 00:43:01,520
	that is high probability right we're

	1155
	00:42:59,160 --> 00:43:03,359
	getting it generated a lot but also low

	1156
	00:43:01,520 --> 00:43:06,079
	risk not a lot of the other outputs in

	1157
	00:43:03,359 --> 00:43:08,440
	our in our set disagree with this

	1158
	00:43:06,079 --> 00:43:10,359
	answer so those are a couple of

	1159
	00:43:08,440 --> 00:43:11,920
	different variants of methods where

	1160
	00:43:10,359 --> 00:43:13,880
	we're sort of sampling a wide set of

	1161
	00:43:11,920 --> 00:43:17,359
	sequences and trying to choose the best

	1162
	00:43:13,880 --> 00:43:20,960
	one um MBR is one set is one type of

	1163
	00:43:17,359 --> 00:43:22,680
	sort of sequence set reranking method um

	1164
	00:43:20,960 --> 00:43:24,760
	you could do other things to rerank sets

	1165
	00:43:22,680 --> 00:43:27,400
	as well but this is sort of one

	1166
	00:43:24,760 --> 00:43:30,359
	representative class of these yes uh or

	1167
	00:43:27,400 --> 00:43:32,280
	of the of these methods before we get

	1168
	00:43:30,359 --> 00:43:35,200
	into constrain generation those are sort

	1169
	00:43:32,280 --> 00:43:37,000
	of the three broad categories of

	1170
	00:43:35,200 --> 00:43:39,480
	inference methods we'll discuss which is

	1171
	00:43:37,000 --> 00:43:41,680
	sort of sampling from some distribution

	1172
	00:43:39,480 --> 00:43:45,040
	searching over some space of

	1173
	00:43:41,680 --> 00:43:47,400
	distributions and doing some kind of um

	1174
	00:43:45,040 --> 00:43:48,559
	analysis over a set of samples to choose

	1175
	00:43:47,400 --> 00:43:51,359
	which ones they

	1176
	00:43:48,559 --> 00:43:52,559
	return um does anyone have any questions

	1177
	00:43:51,359 --> 00:43:55,079
	at this

	1178
	00:43:52,559 --> 00:44:00,680
	point

	1179
	00:43:55,079 --> 00:44:00,680
	yeah that a model

	1180
	00:44:05,800 --> 00:44:12,760
	cannot yeah like why is averaging model

	1181
	00:44:08,359 --> 00:44:16,400
	weights not MBR um I think it's not MBR

	1182
	00:44:12,760 --> 00:44:18,559
	because the two um the key thing that I

	1183
	00:44:16,400 --> 00:44:20,880
	think really makes a method MBR is this

	1184
	00:44:18,559 --> 00:44:22,480
	concept of comparing between multiple um

	1185
	00:44:20,880 --> 00:44:24,880
	sort of pseudo

	1186
	00:44:22,480 --> 00:44:26,839
	references um and there you don't have

	1187
	00:44:24,880 --> 00:44:28,359
	the same like you aage model way can you

	1188
	00:44:26,839 --> 00:44:32,440
	wind up with sort of a single output on

	1189
	00:44:28,359 --> 00:44:34,040
	the end that maybe is like using like

	1190
	00:44:32,440 --> 00:44:35,800
	information from these two model

	1191
	00:44:34,040 --> 00:44:38,240
	distributions that you've sort of smush

	1192
	00:44:35,800 --> 00:44:41,160
	together um but it's not the same

	1193
	00:44:38,240 --> 00:44:44,720
	concept of like comparing against pseudo

	1194
	00:44:41,160 --> 00:44:44,720
	references or ranking in a

	1195
	00:44:48,920 --> 00:44:55,599
	set right so now this is sort of a this

	1196
	00:44:52,720 --> 00:44:57,559
	was a wide variety of methods to try to

	1197
	00:44:55,599 --> 00:44:59,040
	find an output that's just sort of good

	1198
	00:44:57,559 --> 00:45:01,440
	right we want an output that that is

	1199
	00:44:59,040 --> 00:45:03,480
	nice out of our model um but now we'd

	1200
	00:45:01,440 --> 00:45:05,880
	like to maybe enclose a few additional

	1201
	00:45:03,480 --> 00:45:08,280
	constraints so say I'm asking our model

	1202
	00:45:05,880 --> 00:45:10,720
	for some Hobbies I could use to stay in

	1203
	00:45:08,280 --> 00:45:11,920
	to stay in shape and no matter what I

	1204
	00:45:10,720 --> 00:45:14,160
	don't want the model to recommend

	1205
	00:45:11,920 --> 00:45:16,880
	climbing like I I just I don't want this

	1206
	00:45:14,160 --> 00:45:18,400
	as an option I've tried it I'm not a fan

	1207
	00:45:16,880 --> 00:45:21,240
	um how do I get the model to stop

	1208
	00:45:18,400 --> 00:45:22,760
	suggesting climbing to me and if you've

	1209
	00:45:21,240 --> 00:45:24,559
	sort of played around with some of the

	1210
	00:45:22,760 --> 00:45:26,200
	more recent llms you'd say maybe this is

	1211
	00:45:24,559 --> 00:45:27,480
	easy right you just tell the model the

	1212
	00:45:26,200 --> 00:45:30,160
	instruction that you don't want to talk

	1213
	00:45:27,480 --> 00:45:31,640
	about climbing and having talked to Bard

	1214
	00:45:30,160 --> 00:45:33,640
	recently I can tell you unfortunately

	1215
	00:45:31,640 --> 00:45:34,800
	that it's not that easy so I tell the

	1216
	00:45:33,640 --> 00:45:36,599
	model I don't want to talk about

	1217
	00:45:34,800 --> 00:45:38,000
	climbing it does okay for a little bit

	1218
	00:45:36,599 --> 00:45:40,920
	and then it's like all right but maybe

	1219
	00:45:38,000 --> 00:45:42,359
	you want to try rap climbing um and so

	1220
	00:45:40,920 --> 00:45:44,559
	we could continue trying to instruction

	1221
	00:45:42,359 --> 00:45:46,200
	to our model but maybe there's sort of a

	1222
	00:45:44,559 --> 00:45:49,079
	way to impose this constraint on the

	1223
	00:45:46,200 --> 00:45:50,680
	decoding side instead and so I'd say all

	1224
	00:45:49,079 --> 00:45:52,960
	right I'm going to do something dramatic

	1225
	00:45:50,680 --> 00:45:54,440
	right I know I can manipulate the

	1226
	00:45:52,960 --> 00:45:56,200
	probability distribution I'm just going

	1227
	00:45:54,440 --> 00:45:57,920
	to set the probability of climbing to be

	1228
	00:45:56,200 --> 00:46:00,440
	zero I don't want to see this token like

	1229
	00:45:57,920 --> 00:46:02,640
	I'm I'm completely over it um and this

	1230
	00:46:00,440 --> 00:46:04,839
	is sort of nice in some sense because

	1231
	00:46:02,640 --> 00:46:06,720
	this is pretty easy to do um remember

	1232
	00:46:04,839 --> 00:46:08,440
	we're doing a soft Max over the outputs

	1233
	00:46:06,720 --> 00:46:10,599
	to choose this probability distribution

	1234
	00:46:08,440 --> 00:46:12,400
	and so if we add a huge negative number

	1235
	00:46:10,599 --> 00:46:14,160
	to the logic for climbing before we do

	1236
	00:46:12,400 --> 00:46:15,520
	this softmax its probability is going to

	1237
	00:46:14,160 --> 00:46:18,640
	be basically zero and we're never going

	1238
	00:46:15,520 --> 00:46:20,240
	to see it as an output um but this

	1239
	00:46:18,640 --> 00:46:22,480
	doesn't seem like a perfect solution

	1240
	00:46:20,240 --> 00:46:24,400
	right because you know what if the model

	1241
	00:46:22,480 --> 00:46:26,160
	recommends bouldering to me do I have to

	1242
	00:46:24,400 --> 00:46:28,599
	write like a sort of a list of every

	1243
	00:46:26,160 --> 00:46:30,599
	possible climbing synonym in the world

	1244
	00:46:28,599 --> 00:46:32,079
	um what if there's sort of an allowable

	1245
	00:46:30,599 --> 00:46:33,920
	way to use this token like I want the

	1246
	00:46:32,079 --> 00:46:35,319
	model to suggest hiking because climbing

	1247
	00:46:33,920 --> 00:46:37,480
	up a mountain to see a good view is

	1248
	00:46:35,319 --> 00:46:38,720
	relaxing but that's a use of the word

	1249
	00:46:37,480 --> 00:46:41,400
	climbing and we just said that we can't

	1250
	00:46:38,720 --> 00:46:43,520
	use the word climbing um or what if we

	1251
	00:46:41,400 --> 00:46:45,480
	sort of generate other related terms

	1252
	00:46:43,520 --> 00:46:47,520
	before we get to the restricted term

	1253
	00:46:45,480 --> 00:46:49,359
	like the model starts suggesting maybe

	1254
	00:46:47,520 --> 00:46:51,480
	you can work out by going to an indoor

	1255
	00:46:49,359 --> 00:46:52,920
	rock blank and then what are we going to

	1256
	00:46:51,480 --> 00:46:54,800
	say there's not we can't say rock

	1257
	00:46:52,920 --> 00:46:57,079
	climbing so maybe the model suggests

	1258
	00:46:54,800 --> 00:46:58,640
	rock climbing is rock collecting is a

	1259
	00:46:57,079 --> 00:47:01,400
	hobby to stay in shape and that doesn't

	1260
	00:46:58,640 --> 00:47:03,480
	sound good either um you could continue

	1261
	00:47:01,400 --> 00:47:05,640
	like sort of engineering more and more

	1262
	00:47:03,480 --> 00:47:06,599
	complicated rules here but maybe we

	1263
	00:47:05,640 --> 00:47:08,760
	could do something that's a little

	1264
	00:47:06,599 --> 00:47:10,559
	simpler so what if I just sample a bunch

	1265
	00:47:08,760 --> 00:47:11,920
	of outputs from the model and then I

	1266
	00:47:10,559 --> 00:47:14,359
	check if they're about climbing and I

	1267
	00:47:11,920 --> 00:47:16,280
	get rid of them if they are right um

	1268
	00:47:14,359 --> 00:47:18,200
	this is sort of the advantage that it's

	1269
	00:47:16,280 --> 00:47:19,599
	pretty easy to check after the fact if

	1270
	00:47:18,200 --> 00:47:22,480
	the sequence has satisfied this

	1271
	00:47:19,599 --> 00:47:24,400
	constraint you know we could train some

	1272
	00:47:22,480 --> 00:47:26,200
	smaller model to guess if the topic of a

	1273
	00:47:24,400 --> 00:47:27,960
	sentence is about climbing could check

	1274
	00:47:26,200 --> 00:47:30,040
	for keywords we could have a friend

	1275
	00:47:27,960 --> 00:47:31,359
	who's willing to see this content like

	1276
	00:47:30,040 --> 00:47:33,040
	filter through it and throw everything

	1277
	00:47:31,359 --> 00:47:36,480
	out that's not about climing that is

	1278
	00:47:33,040 --> 00:47:38,280
	about climbing but if this model um

	1279
	00:47:36,480 --> 00:47:40,119
	ascribes really high likelihood to this

	1280
	00:47:38,280 --> 00:47:42,559
	like if this model was trained on you

	1281
	00:47:40,119 --> 00:47:44,760
	know data from CS PhD students this

	1282
	00:47:42,559 --> 00:47:46,240
	could be an extremely high likelihood

	1283
	00:47:44,760 --> 00:47:48,319
	suggestion and so we might need to

	1284
	00:47:46,240 --> 00:47:49,839
	regenerate hundreds or thousands of

	1285
	00:47:48,319 --> 00:47:52,559
	sequences to find something that's not

	1286
	00:47:49,839 --> 00:47:55,240
	about climing um and that feels a little

	1287
	00:47:52,559 --> 00:47:56,920
	bit inefficient right so is there

	1288
	00:47:55,240 --> 00:47:59,040
	something that we can do that's a little

	1289
	00:47:56,920 --> 00:48:01,599
	bit better than that well really we'd

	1290
	00:47:59,040 --> 00:48:03,200
	like to guess at some point during our

	1291
	00:48:01,599 --> 00:48:05,200
	generation if the sequence is going to

	1292
	00:48:03,200 --> 00:48:08,000
	be about climbing and maybe like

	1293
	00:48:05,200 --> 00:48:10,640
	recalibrate or you know we could even

	1294
	00:48:08,000 --> 00:48:12,079
	restart or sort of shape Our Generations

	1295
	00:48:10,640 --> 00:48:14,520
	so that we don't wind up with a sequence

	1296
	00:48:12,079 --> 00:48:16,319
	that's about climbing in the first place

	1297
	00:48:14,520 --> 00:48:19,359
	um one of the methods that we'll discuss

	1298
	00:48:16,319 --> 00:48:20,920
	to do this is a method called fudge um

	1299
	00:48:19,359 --> 00:48:22,800
	and unfortunately in their paper they

	1300
	00:48:20,920 --> 00:48:24,240
	don't have the same anti-climbing bias I

	1301
	00:48:22,800 --> 00:48:27,000
	do so this example is actually about

	1302
	00:48:24,240 --> 00:48:29,000
	formality instead um the idea here is

	1303
	00:48:27,000 --> 00:48:32,079
	that we want a sequence output of the

	1304
	00:48:29,000 --> 00:48:34,079
	model that is sort of satisfies this

	1305
	00:48:32,079 --> 00:48:36,079
	constraint of being formal and the way

	1306
	00:48:34,079 --> 00:48:39,960
	we're going to do this is at each step

	1307
	00:48:36,079 --> 00:48:41,640
	of prediction we get the outputs of what

	1308
	00:48:39,960 --> 00:48:44,160
	the model predicts is the next token

	1309
	00:48:41,640 --> 00:48:47,319
	right this sort of distribution here in

	1310
	00:48:44,160 --> 00:48:49,760
	blue and we also have some second

	1311
	00:48:47,319 --> 00:48:52,079
	distribution which says given sort of

	1312
	00:48:49,760 --> 00:48:54,480
	what we have so far How likely is this

	1313
	00:48:52,079 --> 00:48:56,920
	to be a formal sentence at the end right

	1314
	00:48:54,480 --> 00:48:58,880
	does a sentence that starts do you want

	1315
	00:48:56,920 --> 00:49:01,200
	have a high likelihood of being formal

	1316
	00:48:58,880 --> 00:49:04,559
	versus a sentence that starts do you

	1317
	00:49:01,200 --> 00:49:07,200
	prefer and so this sort of guess at what

	1318
	00:49:04,559 --> 00:49:09,520
	will be formal at the end of the um

	1319
	00:49:07,200 --> 00:49:10,960
	generation will put High likelihood on

	1320
	00:49:09,520 --> 00:49:13,599
	things that result in really formal

	1321
	00:49:10,960 --> 00:49:15,880
	sentences like do you prefer or do you

	1322
	00:49:13,599 --> 00:49:17,200
	thus whereas the original model might

	1323
	00:49:15,880 --> 00:49:19,440
	have higher likelihood on things that

	1324
	00:49:17,200 --> 00:49:22,559
	are maybe more commonly said like do you

	1325
	00:49:19,440 --> 00:49:24,319
	want um so we combine these two

	1326
	00:49:22,559 --> 00:49:26,280
	distributions you can just multiply them

	1327
	00:49:24,319 --> 00:49:29,079
	together and then we sample from this

	1328
	00:49:26,280 --> 00:49:30,520
	modified distribution which now has some

	1329
	00:49:29,079 --> 00:49:32,359
	sort of high weight on things that the

	1330
	00:49:30,520 --> 00:49:33,559
	model thinks are likely but also takes

	1331
	00:49:32,359 --> 00:49:35,960
	into account the likelihood of

	1332
	00:49:33,559 --> 00:49:38,240
	satisfying a constraint um this is

	1333
	00:49:35,960 --> 00:49:40,640
	another sort of method of modifying or

	1334
	00:49:38,240 --> 00:49:42,520
	sampling distribution um with some

	1335
	00:49:40,640 --> 00:49:44,520
	external information here and so there's

	1336
	00:49:42,520 --> 00:49:47,440
	results and sequences that wind up being

	1337
	00:49:44,520 --> 00:49:48,799
	sort of more likely to be formal without

	1338
	00:49:47,440 --> 00:49:50,280
	having to sample a whole bunch of

	1339
	00:49:48,799 --> 00:49:52,880
	sentences and reject the ones that we

	1340
	00:49:50,280 --> 00:49:54,720
	think don't satisfy this constraint so

	1341
	00:49:52,880 --> 00:49:57,119
	how do we get sort of a guess of what

	1342
	00:49:54,720 --> 00:49:58,839
	will be formal at the end of Generation

	1343
	00:49:57,119 --> 00:50:01,319
	Um this is where the name fudge comes

	1344
	00:49:58,839 --> 00:50:03,319
	from the fud stands for future

	1345
	00:50:01,319 --> 00:50:06,640
	discriminator and so what they do is

	1346
	00:50:03,319 --> 00:50:08,920
	they train a model on prefixes to guess

	1347
	00:50:06,640 --> 00:50:10,400
	whether that sequence will be formal um

	1348
	00:50:08,920 --> 00:50:12,040
	you can do this if you have a bunch of

	1349
	00:50:10,400 --> 00:50:15,319
	data that's sort of sorted into formal

	1350
	00:50:12,040 --> 00:50:17,720
	and not formal right every um sort of

	1351
	00:50:15,319 --> 00:50:20,119
	prefix of a sentence in the formal

	1352
	00:50:17,720 --> 00:50:21,480
	category is a training example right you

	1353
	00:50:20,119 --> 00:50:23,720
	know a sentence that starts do you

	1354
	00:50:21,480 --> 00:50:27,599
	prefer you can shop off each token to

	1355
	00:50:23,720 --> 00:50:29,920
	get sort of a um set of sequ of prefixes

	1356
	00:50:27,599 --> 00:50:31,160
	to sequences that have the label formal

	1357
	00:50:29,920 --> 00:50:33,559
	and you can do the same thing to your

	1358
	00:50:31,160 --> 00:50:34,920
	informal set and train a discriminator

	1359
	00:50:33,559 --> 00:50:36,559
	to choose between them to say like

	1360
	00:50:34,920 --> 00:50:38,400
	what's the probability the sentence but

	1361
	00:50:36,559 --> 00:50:41,160
	will belong to the formal set when we

	1362
	00:50:38,400 --> 00:50:43,319
	finish and so this idea of sort of

	1363
	00:50:41,160 --> 00:50:44,359
	trying to guess at a given decoding step

	1364
	00:50:43,319 --> 00:50:49,480
	if we're going to wind up with our

	1365
	00:50:44,359 --> 00:50:50,799
	constraints satisfied at the end um is a

	1366
	00:50:49,480 --> 00:50:53,000
	sort of key way to do constraint

	1367
	00:50:50,799 --> 00:50:56,000
	decoding um and one that we'll return to

	1368
	00:50:53,000 --> 00:50:58,280
	in just a couple slides here

	1369
	00:50:56,000 --> 00:51:00,440
	I want to talk touch on something

	1370
	00:50:58,280 --> 00:51:03,079
	slightly different which is that maybe

	1371
	00:51:00,440 --> 00:51:04,599
	one of the constraints we care about is

	1372
	00:51:03,079 --> 00:51:07,319
	something a little more nebulous like we

	1373
	00:51:04,599 --> 00:51:09,160
	want to match human preference um the

	1374
	00:51:07,319 --> 00:51:12,079
	way that we usually accomplish this

	1375
	00:51:09,160 --> 00:51:14,920
	constraint is a little bit different

	1376
	00:51:12,079 --> 00:51:16,040
	right um this we' usually do through

	1377
	00:51:14,920 --> 00:51:18,839
	like reinforcement learning through

	1378
	00:51:16,040 --> 00:51:21,559
	human feedback um and so we take sort of

	1379
	00:51:18,839 --> 00:51:24,960
	our original model distribution and we

	1380
	00:51:21,559 --> 00:51:27,960
	take a sort of really like tight like

	1381
	00:51:24,960 --> 00:51:30,200
	distrib tion of evidence that says like

	1382
	00:51:27,960 --> 00:51:31,680
	um this model says that this sequence is

	1383
	00:51:30,200 --> 00:51:33,960
	really high reward this sequence is

	1384
	00:51:31,680 --> 00:51:35,640
	really low reward and we try to sort of

	1385
	00:51:33,960 --> 00:51:38,200
	combine them somehow through training so

	1386
	00:51:35,640 --> 00:51:41,240
	we get a new model that is um quote

	1387
	00:51:38,200 --> 00:51:43,240
	unquote aligned and that it has like a

	1388
	00:51:41,240 --> 00:51:45,280
	higher likelihood of giving us things

	1389
	00:51:43,240 --> 00:51:48,640
	that have really high reward according

	1390
	00:51:45,280 --> 00:51:51,319
	to our reward distribution um you can

	1391
	00:51:48,640 --> 00:51:53,599
	view this though as a type of basian

	1392
	00:51:51,319 --> 00:51:55,119
	inference and so what this means is the

	1393
	00:51:53,599 --> 00:51:57,440
	distribution that we really want to get

	1394
	00:51:55,119 --> 00:51:59,880
	at the end is a distribution that

	1395
	00:51:57,440 --> 00:52:03,160
	combines our original models

	1396
	00:51:59,880 --> 00:52:05,680
	distribution and some idea of like How

	1397
	00:52:03,160 --> 00:52:08,480
	likely we are to satisfy the reward

	1398
	00:52:05,680 --> 00:52:10,720
	right um this we do through

	1399
	00:52:08,480 --> 00:52:12,359
	reinforcement learning but if we sort of

	1400
	00:52:10,720 --> 00:52:14,480
	know what these two distributions look

	1401
	00:52:12,359 --> 00:52:16,119
	like we've we've just been talking about

	1402
	00:52:14,480 --> 00:52:17,680
	a lot of methods that modify the

	1403
	00:52:16,119 --> 00:52:20,119
	original models distribution with

	1404
	00:52:17,680 --> 00:52:21,880
	external information it seems like maybe

	1405
	00:52:20,119 --> 00:52:24,760
	we could just add that external

	1406
	00:52:21,880 --> 00:52:26,200
	information in at decoding time to get

	1407
	00:52:24,760 --> 00:52:29,040
	some of the same

	1408
	00:52:26,200 --> 00:52:31,040
	effects um and it turns out you can do

	1409
	00:52:29,040 --> 00:52:32,799
	exactly this so this is a paper from

	1410
	00:52:31,040 --> 00:52:36,680
	last year called reward augmented

	1411
	00:52:32,799 --> 00:52:39,079
	decoding and the idea here is sort of um

	1412
	00:52:36,680 --> 00:52:41,839
	in the same conceptual class as fudge

	1413
	00:52:39,079 --> 00:52:44,079
	but instead of um predicting whether

	1414
	00:52:41,839 --> 00:52:46,079
	we're likely to satisfy the constraint

	1415
	00:52:44,079 --> 00:52:47,599
	we're predicting how much reward we

	1416
	00:52:46,079 --> 00:52:49,880
	think that sequence will have at the end

	1417
	00:52:47,599 --> 00:52:52,599
	of generation so we take our original

	1418
	00:52:49,880 --> 00:52:54,839
	model without doing any rhf and we get

	1419
	00:52:52,599 --> 00:52:58,160
	the output we get the predictions for

	1420
	00:52:54,839 --> 00:52:59,400
	the next token and then we use a model

	1421
	00:52:58,160 --> 00:53:02,359
	that's been trained to predict the

	1422
	00:52:59,400 --> 00:53:05,040
	likely reward given some prefix like a

	1423
	00:53:02,359 --> 00:53:06,720
	future discriminator and we calculate

	1424
	00:53:05,040 --> 00:53:08,200
	the likely reward if we pick each of

	1425
	00:53:06,720 --> 00:53:09,799
	those tokens and then we use the

	1426
	00:53:08,200 --> 00:53:12,319
	combination of those two distributions

	1427
	00:53:09,799 --> 00:53:13,720
	to choose what to decode next um and

	1428
	00:53:12,319 --> 00:53:16,000
	this sort of gives you some of the

	1429
	00:53:13,720 --> 00:53:18,440
	benefits of rlf without actually having

	1430
	00:53:16,000 --> 00:53:21,200
	to do reinforcement learning so it's a

	1431
	00:53:18,440 --> 00:53:23,160
	way of treating like aligning to human

	1432
	00:53:21,200 --> 00:53:26,839
	feedback as just another constraint that

	1433
	00:53:23,160 --> 00:53:30,400
	you can impose at decoding point

	1434
	00:53:26,839 --> 00:53:32,319
	so those were sort of a a subset of the

	1435
	00:53:30,400 --> 00:53:34,280
	um constrains decoding strategies that

	1436
	00:53:32,319 --> 00:53:35,799
	people use um before we get into the

	1437
	00:53:34,280 --> 00:53:38,400
	human and the loop stack are there any

	1438
	00:53:35,799 --> 00:53:38,400
	questions on

	1439
	00:53:39,040 --> 00:53:43,599
	this yes for

	1440
	00:53:44,960 --> 00:53:48,319
	the do you have

	1441
	00:53:52,799 --> 00:53:57,440
	to right so for the discrimin do you

	1442
	00:53:55,640 --> 00:54:00,000
	need to train one for every constraint

	1443
	00:53:57,440 --> 00:54:01,440
	and you do yeah so you need to have some

	1444
	00:54:00,000 --> 00:54:02,920
	set of data that satisfies your

	1445
	00:54:01,440 --> 00:54:05,319
	constraint and some set of data that

	1446
	00:54:02,920 --> 00:54:08,200
	doesn't before you can enforce a new

	1447
	00:54:05,319 --> 00:54:10,200
	constraint in an alternative might be

	1448
	00:54:08,200 --> 00:54:12,040
	like in the paper that's what they did

	1449
	00:54:10,200 --> 00:54:16,400
	but an alternative might be just to

	1450
	00:54:12,040 --> 00:54:18,359
	train a discriminator to determine

	1451
	00:54:16,400 --> 00:54:20,880
	whether any constraint was violated so

	1452
	00:54:18,359 --> 00:54:23,359
	if you have 100 constraints you could do

	1453
	00:54:20,880 --> 00:54:25,599
	a binary prier about whether any

	1454
	00:54:23,359 --> 00:54:26,880
	constraint is violated and then

	1455
	00:54:25,599 --> 00:54:29,040
	also

	1456
	00:54:26,880 --> 00:54:30,559
	sufficient but if you wanted to add a

	1457
	00:54:29,040 --> 00:54:34,079
	new constraint you'd still have to

	1458
	00:54:30,559 --> 00:54:34,079
	retrain or you have to retrain

	1459
	00:54:35,160 --> 00:54:41,319
	or the the reason that this is sort of

	1460
	00:54:38,119 --> 00:54:43,119
	relatively reasonable to do is that this

	1461
	00:54:41,319 --> 00:54:45,240
	determination of if a constraint is

	1462
	00:54:43,119 --> 00:54:46,960
	likely to be violated is sort of a a

	1463
	00:54:45,240 --> 00:54:48,520
	lighter weight or an easier task to

	1464
	00:54:46,960 --> 00:54:50,520
	learn you can use a relatively small

	1465
	00:54:48,520 --> 00:54:52,079
	model for this versus like your big

	1466
	00:54:50,520 --> 00:54:53,680
	model just that has to be able to

	1467
	00:54:52,079 --> 00:54:55,920
	predict the next token for any sequence

	1468
	00:54:53,680 --> 00:54:58,400
	anymore yeah another another like

	1469
	00:54:55,920 --> 00:55:00,760
	interesting thing is if you think about

	1470
	00:54:58,400 --> 00:55:01,520
	it normally you're predicting with your

	1471
	00:55:00,760 --> 00:55:04,119
	big

	1472
	00:55:01,520 --> 00:55:06,359
	softmax like this over all of your

	1473
	00:55:04,119 --> 00:55:09,680
	vocabulary you can even use the same

	1474
	00:55:06,359 --> 00:55:11,920
	representations here to predict with a

	1475
	00:55:09,680 --> 00:55:13,359
	binary classifier uh whether the

	1476
	00:55:11,920 --> 00:55:14,559
	constraint is violated let's say you

	1477
	00:55:13,359 --> 00:55:17,240
	have 100

	1478
	00:55:14,559 --> 00:55:19,240
	constraints this is still a vector of

	1479
	00:55:17,240 --> 00:55:21,520
	size 100 compared to your vector of size

	1480
	00:55:19,240 --> 00:55:26,240
	32,000 that you're using for llama right

	1481
	00:55:21,520 --> 00:55:28,280
	so it's not like this adds the training

	1482
	00:55:26,240 --> 00:55:32,799
	would cost some time but it adds very

	1483
	00:55:28,280 --> 00:55:32,799
	little like inference time I guess

	1484
	00:55:33,440 --> 00:55:38,960
	basically the rock

	1485
	00:55:35,880 --> 00:55:41,400
	sound so when you do the constraint you

	1486
	00:55:38,960 --> 00:55:43,160
	use like a more General

	1487
	00:55:41,400 --> 00:55:44,680
	like do

	1488
	00:55:43,160 --> 00:55:48,160
	notest

	1489
	00:55:44,680 --> 00:55:50,799
	or I guess like in that constraint for

	1490
	00:55:48,160 --> 00:55:50,799
	you can add

	1491
	00:55:52,559 --> 00:55:57,000
	like, is there

	1492
	00:55:57,880 --> 00:56:00,720
	like is there a way to generalize your

	1493
	00:55:59,400 --> 00:56:04,760
	constraint would be like don't talk

	1494
	00:56:00,720 --> 00:56:07,039
	about this whole set of hobes um you

	1495
	00:56:04,760 --> 00:56:08,960
	could do that by training a

	1496
	00:56:07,039 --> 00:56:10,400
	discriminator um by training one

	1497
	00:56:08,960 --> 00:56:12,359
	discriminator that considers all of

	1498
	00:56:10,400 --> 00:56:15,119
	those or by training like a hundred

	1499
	00:56:12,359 --> 00:56:17,559
	different discriminators and then um

	1500
	00:56:15,119 --> 00:56:19,520
	sort of taking like the maximum score

	1501
	00:56:17,559 --> 00:56:21,240
	from any of them right like you want to

	1502
	00:56:19,520 --> 00:56:23,240
	you want to be able to exclude all of

	1503
	00:56:21,240 --> 00:56:27,799
	these things so you consider if any of

	1504
	00:56:23,240 --> 00:56:30,720
	them are violated yeah and for um reward

	1505
	00:56:27,799 --> 00:56:32,839
	augmented recoding how do we sort of

	1506
	00:56:30,720 --> 00:56:36,039
	like frame that reward model or is that

	1507
	00:56:32,839 --> 00:56:38,400
	just come from the previously done rhf

	1508
	00:56:36,039 --> 00:56:41,079
	data that the store from there and then

	1509
	00:56:38,400 --> 00:56:44,119
	you sort of like FR another

	1510
	00:56:41,079 --> 00:56:47,880
	discriminator but this one

	1511
	00:56:44,119 --> 00:56:50,799
	now I I fully understand yeah so how do

	1512
	00:56:47,880 --> 00:56:52,920
	we get the the reward model here this is

	1513
	00:56:50,799 --> 00:56:55,280
	we can use the same data that we' use

	1514
	00:56:52,920 --> 00:56:58,000
	for rhf but we need a slightly different

	1515
	00:56:55,280 --> 00:57:01,119
	model so for rhf we'll train a reward

	1516
	00:56:58,000 --> 00:57:02,599
	model over full sequences right and here

	1517
	00:57:01,119 --> 00:57:05,280
	we need to do the same trick where we

	1518
	00:57:02,599 --> 00:57:07,280
	sort of look at just prefixes and try to

	1519
	00:57:05,280 --> 00:57:09,640
	guess the reward Downstream but if we

	1520
	00:57:07,280 --> 00:57:12,440
	have already have preference data then

	1521
	00:57:09,640 --> 00:57:15,119
	we have some um like we have a data

	1522
	00:57:12,440 --> 00:57:16,720
	source to do this with I think if I'm

	1523
	00:57:15,119 --> 00:57:19,240
	remembering correctly they also had a

	1524
	00:57:16,720 --> 00:57:20,920
	couple more sort of tricks for data

	1525
	00:57:19,240 --> 00:57:22,640
	augmentation to get this to work this is

	1526
	00:57:20,920 --> 00:57:25,720
	sort of like a non-trivial thing to

	1527
	00:57:22,640 --> 00:57:28,039
	figure out um because like reward is

	1528
	00:57:25,720 --> 00:57:30,200
	generally a secret bual

	1529
	00:57:28,039 --> 00:57:32,280
	attribute and also if you don't know

	1530
	00:57:30,200 --> 00:57:34,160
	very much about rhf we're going to cover

	1531
	00:57:32,280 --> 00:57:36,400
	that the future class so don't worry if

	1532
	00:57:34,160 --> 00:57:37,880
	this is a yeah sorry to Jump Ahead a

	1533
	00:57:36,400 --> 00:57:39,880
	little no no

	1534
	00:57:37,880 --> 00:57:43,640
	wores

	1535
	00:57:39,880 --> 00:57:47,240
	yeah application this like why would we

	1536
	00:57:43,640 --> 00:57:49,640
	doing this to ensure it could be like

	1537
	00:57:47,240 --> 00:57:52,839
	our llm would want to highlight certain

	1538
	00:57:49,640 --> 00:57:53,799
	qualities like we want our evence to be

	1539
	00:57:52,839 --> 00:57:55,960
	more

	1540
	00:57:53,799 --> 00:57:57,839
	empathetic is there

	1541
	00:57:55,960 --> 00:57:59,440
	something yeah like what are the real

	1542
	00:57:57,839 --> 00:58:01,280
	world applications like could we use

	1543
	00:57:59,440 --> 00:58:03,680
	this to make L more empathetic or

	1544
	00:58:01,280 --> 00:58:06,359
	something yeah any any real attribute

	1545
	00:58:03,680 --> 00:58:08,000
	that you can sort of collect like

	1546
	00:58:06,359 --> 00:58:09,839
	positive and negative data for you could

	1547
	00:58:08,000 --> 00:58:12,200
	do this kind of constraints for I think

	1548
	00:58:09,839 --> 00:58:15,119
	the the ones you see most commonly are

	1549
	00:58:12,200 --> 00:58:16,480
	the human preference and then like

	1550
	00:58:15,119 --> 00:58:18,839
	negative constraints like you don't want

	1551
	00:58:16,480 --> 00:58:20,000
	your model to generate offensive content

	1552
	00:58:18,839 --> 00:58:21,839
	and if you can build like a good

	1553
	00:58:20,000 --> 00:58:23,319
	discriminator for is a sentence going in

	1554
	00:58:21,839 --> 00:58:26,160
	a really offensive Direction you can

	1555
	00:58:23,319 --> 00:58:28,440
	kind of stop it from gener

	1556
	00:58:26,160 --> 00:58:30,480
	yeah would it be a good idea if you

	1557
	00:58:28,440 --> 00:58:33,760
	generate a bunch of cons and ask the

	1558
	00:58:30,480 --> 00:58:35,480
	model itself whether it violates the

	1559
	00:58:33,760 --> 00:58:37,319
	yeah you could do that for sure could

	1560
	00:58:35,480 --> 00:58:38,920
	you ask like could you generate a bunch

	1561
	00:58:37,319 --> 00:58:42,440
	of samples and ask the model if it

	1562
	00:58:38,920 --> 00:58:44,720
	violates the constraint um this is also

	1563
	00:58:42,440 --> 00:58:47,119
	a type of sort of sample and then rerank

	1564
	00:58:44,720 --> 00:58:52,319
	strategy um but yeah this would be sort

	1565
	00:58:47,119 --> 00:58:54,000
	of a more um clever like less

	1566
	00:58:52,319 --> 00:58:55,559
	heavyweight version of this checking if

	1567
	00:58:54,000 --> 00:58:57,319
	it's about climate means right you'd

	1568
	00:58:55,559 --> 00:58:58,520
	like ask the model if it violated the

	1569
	00:58:57,319 --> 00:59:00,160
	constraint and if it's a good enough

	1570
	00:58:58,520 --> 00:59:02,480
	model it could probably do that pretty

	1571
	00:59:00,160 --> 00:59:05,160
	well I suppose in that case you don't

	1572
	00:59:02,480 --> 00:59:08,160
	have to thing anything yeah yeah and

	1573
	00:59:05,160 --> 00:59:10,359
	this is sort of a general like the

	1574
	00:59:08,160 --> 00:59:12,240
	generating text that like satisfies a

	1575
	00:59:10,359 --> 00:59:14,079
	constraint is harder than checking if a

	1576
	00:59:12,240 --> 00:59:16,280
	text satisfies a constraint so even if

	1577
	00:59:14,079 --> 00:59:17,880
	the model isn't good about like not

	1578
	00:59:16,280 --> 00:59:19,440
	generating text about climbing when you

	1579
	00:59:17,880 --> 00:59:20,520
	tell it to it might be able to tell if

	1580
	00:59:19,440 --> 00:59:23,640
	text is

	1581
	00:59:20,520 --> 00:59:26,640
	about yeah yeah so how do

	1582
	00:59:23,640 --> 00:59:26,640
	you

	1583
	00:59:28,400 --> 00:59:32,359
	have different

	1584
	00:59:32,920 --> 00:59:36,319
	different you have

	1585
	00:59:36,599 --> 00:59:42,119
	to yeah like how do you collect the data

	1586
	00:59:38,839 --> 00:59:45,720
	to train this discriminator um generally

	1587
	00:59:42,119 --> 00:59:47,160
	you're going to see like you'll look to

	1588
	00:59:45,720 --> 00:59:48,720
	see if there are data sets that already

	1589
	00:59:47,160 --> 00:59:50,160
	captured this attribute or you could

	1590
	00:59:48,720 --> 00:59:51,599
	sort of write her istics to try to

	1591
	00:59:50,160 --> 00:59:53,839
	recover it if it's an attribute that not

	1592
	00:59:51,599 --> 00:59:55,480
	a lot of other people care about like

	1593
	00:59:53,839 --> 00:59:58,280
	you could write your puristic to check

	1594
	00:59:55,480 --> 01:00:00,160
	if text is about climbing for instance

	1595
	00:59:58,280 --> 01:00:02,359
	um and then try to recover what noisy

	1596
	01:00:00,160 --> 01:00:04,200
	samples of data that is or is not about

	1597
	01:00:02,359 --> 01:00:05,559
	climbing maybe you could scrape a

	1598
	01:00:04,200 --> 01:00:07,000
	climbing forum and then scrape like a

	1599
	01:00:05,559 --> 01:00:09,079
	hiking forum and use the difference

	1600
	01:00:07,000 --> 01:00:10,319
	between them um but for a lot of tests

	1601
	01:00:09,079 --> 01:00:11,760
	there's actually pretty good data sets

	1602
	01:00:10,319 --> 01:00:14,400
	already out there for this so there's

	1603
	01:00:11,760 --> 01:00:17,480
	like in there's a lot of style transfer

	1604
	01:00:14,400 --> 01:00:20,200
	tasks that are like go from informal to

	1605
	01:00:17,480 --> 01:00:22,240
	formal or go from this to that or like

	1606
	01:00:20,200 --> 01:00:24,039
	make this text in an iic contamin and

	1607
	01:00:22,240 --> 01:00:26,559
	you can find like data from those

	1608
	01:00:24,039 --> 01:00:26,559
	sources

	1609
	01:00:26,799 --> 01:00:31,599
	we never like talked about F yet but I'm

	1610
	01:00:29,520 --> 01:00:34,520
	really curious with like the word a

	1611
	01:00:31,599 --> 01:00:38,039
	beting whether it would perform better

	1612
	01:00:34,520 --> 01:00:39,079
	than like fineing on RF like certainly

	1613
	01:00:38,039 --> 01:00:42,720
	more

	1614
	01:00:39,079 --> 01:00:45,039
	efficient but I I was I think this is a

	1615
	01:00:42,720 --> 01:00:49,760
	comparison they make in their paper but

	1616
	01:00:45,039 --> 01:00:52,520
	I don't remember their pun on yeah um in

	1617
	01:00:49,760 --> 01:00:55,280
	general there's this sort of a like you

	1618
	01:00:52,520 --> 01:00:57,039
	can pay a onetime kind of heavy cost to

	1619
	01:00:55,280 --> 01:00:58,880
	fine-tune or you can pay costs at

	1620
	01:00:57,039 --> 01:01:01,160
	inference time every time to make sort

	1621
	01:00:58,880 --> 01:01:03,880
	of a to make your model better in any of

	1622
	01:01:01,160 --> 01:01:06,160
	these ways and depending on how much

	1623
	01:01:03,880 --> 01:01:09,119
	inference you're playing do like one or

	1624
	01:01:06,160 --> 01:01:09,119
	the other of these could be

	1625
	01:01:11,240 --> 01:01:16,400
	better

	1626
	01:01:12,839 --> 01:01:19,200
	great so now we're going to talk about

	1627
	01:01:16,400 --> 01:01:21,160
	sort of methods for introducing human

	1628
	01:01:19,200 --> 01:01:22,680
	interaction into the decoding process

	1629
	01:01:21,160 --> 01:01:25,240
	and everything we've looked at so far

	1630
	01:01:22,680 --> 01:01:26,920
	has been very sort of black booss kind

	1631
	01:01:25,240 --> 01:01:28,920
	of hands off right like you give the

	1632
	01:01:26,920 --> 01:01:30,640
	model M some input maybe we do some kind

	1633
	01:01:28,920 --> 01:01:33,640
	of manipulation on the decoding side you

	1634
	01:01:30,640 --> 01:01:37,160
	get one output back right um but in a

	1635
	01:01:33,640 --> 01:01:38,920
	lot of situations where maybe you have

	1636
	01:01:37,160 --> 01:01:40,960
	some high-risk application and you need

	1637
	01:01:38,920 --> 01:01:42,640
	somebody to be consistently monitoring

	1638
	01:01:40,960 --> 01:01:43,799
	and maybe intervening or you're doing

	1639
	01:01:42,640 --> 01:01:46,359
	something where you want to do some kind

	1640
	01:01:43,799 --> 01:01:47,960
	of human AI collaboration um and you

	1641
	01:01:46,359 --> 01:01:49,160
	want to be able to go back and forth or

	1642
	01:01:47,960 --> 01:01:50,960
	you want to have a conversation with the

	1643
	01:01:49,160 --> 01:01:53,480
	model what you're actually doing is sort

	1644
	01:01:50,960 --> 01:01:54,960
	of a series of decodings with human

	1645
	01:01:53,480 --> 01:01:56,319
	intervention in between

	1646
	01:01:54,960 --> 01:01:58,640
	um and I'm going to talk about a couple

	1647
	01:01:56,319 --> 01:02:00,760
	of these strategies briefly I think if

	1648
	01:01:58,640 --> 01:02:02,200
	you've used sort of a modern llm you're

	1649
	01:02:00,760 --> 01:02:04,440
	probably familiar with at least a few of

	1650
	01:02:02,200 --> 01:02:06,720
	them already um we'll sort of put names

	1651
	01:02:04,440 --> 01:02:08,359
	to each of them and the set of examples

	1652
	01:02:06,720 --> 01:02:10,880
	that we're running with here are from a

	1653
	01:02:08,359 --> 01:02:13,880
	paper called wordcraft which is about um

	1654
	01:02:10,880 --> 01:02:15,480
	story generation with llm assistants but

	1655
	01:02:13,880 --> 01:02:17,559
	these can also be applied sort of more

	1656
	01:02:15,480 --> 01:02:20,319
	generally to any kind of task where

	1657
	01:02:17,559 --> 01:02:23,799
	you'd want to go back and forth with a

	1658
	01:02:20,319 --> 01:02:25,319
	model um the sort of easiest or maybe

	1659
	01:02:23,799 --> 01:02:27,599
	simplest place to start here is just

	1660
	01:02:25,319 --> 01:02:29,760
	with interleaving text right you can

	1661
	01:02:27,599 --> 01:02:31,400
	choose when the model starts and stops

	1662
	01:02:29,760 --> 01:02:33,720
	decoding and you can choose when a human

	1663
	01:02:31,400 --> 01:02:34,920
	is writing text in between and you can

	1664
	01:02:33,720 --> 01:02:36,680
	condition your model in sort of a

	1665
	01:02:34,920 --> 01:02:39,240
	mixture of human and model generated

	1666
	01:02:36,680 --> 01:02:41,279
	text to choose what to continue next um

	1667
	01:02:39,240 --> 01:02:43,680
	you can also do something like have the

	1668
	01:02:41,279 --> 01:02:45,319
	model generate a set of text edit that

	1669
	01:02:43,680 --> 01:02:47,119
	text in some way maybe the human is

	1670
	01:02:45,319 --> 01:02:48,640
	imposing some really subtle constraint

	1671
	01:02:47,119 --> 01:02:50,559
	like I want it to sound like my writing

	1672
	01:02:48,640 --> 01:02:52,200
	style we don't have a discriminator for

	1673
	01:02:50,559 --> 01:02:54,119
	this but the human can sort of modify

	1674
	01:02:52,200 --> 01:02:55,680
	the text and then continue generating

	1675
	01:02:54,119 --> 01:02:57,160
	from that point and that will influence

	1676
	01:02:55,680 --> 01:03:01,160
	the style of the text that continues

	1677
	01:02:57,160 --> 01:03:03,240
	being generative um a this case here is

	1678
	01:03:01,160 --> 01:03:04,720
	sort of a you're writing a story

	1679
	01:03:03,240 --> 01:03:06,520
	together and so you're going back and

	1680
	01:03:04,720 --> 01:03:07,799
	forth and editing the text like that but

	1681
	01:03:06,520 --> 01:03:10,319
	you can also think of any kind of

	1682
	01:03:07,799 --> 01:03:11,920
	conversation with a model as the same

	1683
	01:03:10,319 --> 01:03:15,319
	kind of interleaving of text right the

	1684
	01:03:11,920 --> 01:03:17,000
	model gives some um text you provide

	1685
	01:03:15,319 --> 01:03:18,599
	some text you go back and forth on like

	1686
	01:03:17,000 --> 01:03:20,480
	who's providing the text that conditions

	1687
	01:03:18,599 --> 01:03:23,039
	the

	1688
	01:03:20,480 --> 01:03:24,880
	model you also might want to do things

	1689
	01:03:23,039 --> 01:03:26,760
	like more fine brain replace

	1690
	01:03:24,880 --> 01:03:28,559
	so here the person has highlighted some

	1691
	01:03:26,760 --> 01:03:31,640
	text and said like make this more

	1692
	01:03:28,559 --> 01:03:33,960
	descriptive or shorten this to two words

	1693
	01:03:31,640 --> 01:03:36,079
	or maybe you want some additional

	1694
	01:03:33,960 --> 01:03:38,520
	constraint like can this be happier can

	1695
	01:03:36,079 --> 01:03:40,960
	this be sad like change the ending or

	1696
	01:03:38,520 --> 01:03:43,760
	something um you can accomplish this in

	1697
	01:03:40,960 --> 01:03:45,799
	a variety of ways um here this is done

	1698
	01:03:43,760 --> 01:03:47,680
	through input manipulation so you prompt

	1699
	01:03:45,799 --> 01:03:50,359
	your model differently with different

	1700
	01:03:47,680 --> 01:03:52,200
	constraints you can also do this with an

	1701
	01:03:50,359 --> 01:03:54,440
	actual modeling change like if you want

	1702
	01:03:52,200 --> 01:03:56,119
	some kind of infilling model um

	1703
	01:03:54,440 --> 01:03:57,720
	particularly for things like code this

	1704
	01:03:56,119 --> 01:04:01,119
	can be helpful so you want context from

	1705
	01:03:57,720 --> 01:04:02,440
	left and right sides um or you can do

	1706
	01:04:01,119 --> 01:04:03,799
	this with the decoding changes that we

	1707
	01:04:02,440 --> 01:04:05,960
	talked about in the previous section

	1708
	01:04:03,799 --> 01:04:07,799
	right you could add a discriminator for

	1709
	01:04:05,960 --> 01:04:09,680
	descriptiveness of text or you could do

	1710
	01:04:07,799 --> 01:04:11,680
	some kind of sampling ranking method to

	1711
	01:04:09,680 --> 01:04:13,880
	recover a more descriptive

	1712
	01:04:11,680 --> 01:04:16,640
	output another thing that's very common

	1713
	01:04:13,880 --> 01:04:17,960
	in this space is sampling and reranking

	1714
	01:04:16,640 --> 01:04:20,839
	methods where the human is the one

	1715
	01:04:17,960 --> 01:04:23,640
	choosing what to return right so in

	1716
	01:04:20,839 --> 01:04:25,960
	wordcraft you see a set of choices and

	1717
	01:04:23,640 --> 01:04:28,200
	you can choose text to insert but more

	1718
	01:04:25,960 --> 01:04:30,720
	commonly in something like um chat gbt

	1719
	01:04:28,200 --> 01:04:33,160
	or Bard you see this little option to

	1720
	01:04:30,720 --> 01:04:34,880
	regenerate text right you as the human

	1721
	01:04:33,160 --> 01:04:36,160
	can reject the text and say like no I

	1722
	01:04:34,880 --> 01:04:38,680
	don't like this give me a different

	1723
	01:04:36,160 --> 01:04:41,359
	output and this is also sort of a way of

	1724
	01:04:38,680 --> 01:04:44,079
	controlling decoding um just by doing it

	1725
	01:04:41,359 --> 01:04:46,319
	on on a human rather in an algorithmic

	1726
	01:04:44,079 --> 01:04:49,279
	level of course you don't necessarily

	1727
	01:04:46,319 --> 01:04:51,200
	need a human in here and so um some

	1728
	01:04:49,279 --> 01:04:52,960
	recent work has looked at functionally

	1729
	01:04:51,200 --> 01:04:55,799
	using models to make these decisions

	1730
	01:04:52,960 --> 01:04:57,480
	instead um this is a a a prompting paper

	1731
	01:04:55,799 --> 01:05:00,359
	called free of thought which was sort of

	1732
	01:04:57,480 --> 01:05:02,279
	very popular on Twitter last summer um

	1733
	01:05:00,359 --> 01:05:06,119
	and the idea here is that you're going

	1734
	01:05:02,279 --> 01:05:08,480
	to generate um several smaller sequences

	1735
	01:05:06,119 --> 01:05:11,200
	um like a couple of sentences a

	1736
	01:05:08,480 --> 01:05:13,160
	reasoning step or a thought in the paper

	1737
	01:05:11,200 --> 01:05:14,839
	and you're going to use a model to

	1738
	01:05:13,160 --> 01:05:16,839
	choose which ones to continue and you

	1739
	01:05:14,839 --> 01:05:19,000
	can do different sort of constraints

	1740
	01:05:16,839 --> 01:05:21,960
	here like I want to sort of rank this

	1741
	01:05:19,000 --> 01:05:25,079
	set of three or maybe I want to predict

	1742
	01:05:21,960 --> 01:05:26,839
	if any in this set is wrong like is this

	1743
	01:05:25,079 --> 01:05:29,400
	a good reasoning step and if the model

	1744
	01:05:26,839 --> 01:05:32,240
	says no you no longer continue that but

	1745
	01:05:29,400 --> 01:05:33,559
	the idea here is through prompting

	1746
	01:05:32,240 --> 01:05:35,640
	really achieving something that's sort

	1747
	01:05:33,559 --> 01:05:38,960
	of if you squint at it looks a lot like

	1748
	01:05:35,640 --> 01:05:41,279
	beam search right instead of doing a um

	1749
	01:05:38,960 --> 01:05:43,160
	like token level thing and making a

	1750
	01:05:41,279 --> 01:05:45,079
	decision based on likelihood you're

	1751
	01:05:43,160 --> 01:05:47,880
	generating sort of several sentences out

	1752
	01:05:45,079 --> 01:05:50,599
	a time and making a decision based on

	1753
	01:05:47,880 --> 01:05:52,359
	this models feedback right this signal

	1754
	01:05:50,599 --> 01:05:53,799
	from an external source which here is a

	1755
	01:05:52,359 --> 01:05:55,279
	model but could also be a human if

	1756
	01:05:53,799 --> 01:05:57,920
	you're willing willing to sort of wait

	1757
	01:05:55,279 --> 01:06:01,559
	around for them to make the decision and

	1758
	01:05:57,920 --> 01:06:03,839
	so this is a way of sort of giving

	1759
	01:06:01,559 --> 01:06:06,640
	feedback on a broader level than single

	1760
	01:06:03,839 --> 01:06:09,079
	tokens um to guide a decoding process to

	1761
	01:06:06,640 --> 01:06:09,079
	a final

	1762
	01:06:09,839 --> 01:06:15,079
	outut so the last couple of things we'll

	1763
	01:06:12,760 --> 01:06:17,520
	talk about here are sort of practical

	1764
	01:06:15,079 --> 01:06:19,839
	considerations speed choosing decoding

	1765
	01:06:17,520 --> 01:06:22,599
	methods um but I can take any questions

	1766
	01:06:19,839 --> 01:06:22,599
	before that

	1767
	01:06:23,000 --> 01:06:26,000
	to

	1768
	01:06:26,760 --> 01:06:32,920
	great so how do you make this fast and

	1769
	01:06:30,359 --> 01:06:34,920
	in particular if you've ever tried to

	1770
	01:06:32,920 --> 01:06:36,920
	sort of Benchmark performance of a model

	1771
	01:06:34,920 --> 01:06:38,720
	what you realize pretty quickly is that

	1772
	01:06:36,920 --> 01:06:40,720
	the vast majority of time is actually

	1773
	01:06:38,720 --> 01:06:43,440
	spent in decoding you have to generate

	1774
	01:06:40,720 --> 01:06:45,319
	one token at a time you have to sort of

	1775
	01:06:43,440 --> 01:06:46,920
	pass that back through the model to get

	1776
	01:06:45,319 --> 01:06:51,279
	conditioning to generate the next token

	1777
	01:06:46,920 --> 01:06:53,599
	and so this is um generally fairly slow

	1778
	01:06:51,279 --> 01:06:54,839
	um this is sort of a a major impediment

	1779
	01:06:53,599 --> 01:06:56,359
	if you're d to do something like a

	1780
	01:06:54,839 --> 01:06:57,839
	streaming application where you want or

	1781
	01:06:56,359 --> 01:06:59,559
	a chat application where you don't want

	1782
	01:06:57,839 --> 01:07:03,599
	the person to be waiting around for an

	1783
	01:06:59,559 --> 01:07:06,799
	answer um one way to do this is a method

	1784
	01:07:03,599 --> 01:07:09,160
	called Spectra of decoding and this is a

	1785
	01:07:06,799 --> 01:07:12,599
	method where you're using a smaller

	1786
	01:07:09,160 --> 01:07:14,039
	model um not as like we're in contrast

	1787
	01:07:12,599 --> 01:07:16,240
	of decoding right we're using a smaller

	1788
	01:07:14,039 --> 01:07:17,559
	model to decide what not to generate but

	1789
	01:07:16,240 --> 01:07:20,119
	here we're using a smaller model to

	1790
	01:07:17,559 --> 01:07:21,880
	decide be what to generate um and the

	1791
	01:07:20,119 --> 01:07:24,960
	idea here is that most of these tokens

	1792
	01:07:21,880 --> 01:07:26,480
	are maybe not super hard to side it's

	1793
	01:07:24,960 --> 01:07:27,400
	just that occasionally the bigger model

	1794
	01:07:26,480 --> 01:07:30,240
	might want to go in a different

	1795
	01:07:27,400 --> 01:07:32,920
	direction so these green tokens here are

	1796
	01:07:30,240 --> 01:07:35,160
	generated by a smaller model our amateur

	1797
	01:07:32,920 --> 01:07:37,079
	model here and the larger model acts

	1798
	01:07:35,160 --> 01:07:39,960
	largely as a verifier and what it does

	1799
	01:07:37,079 --> 01:07:43,000
	is it checks if the output so far is

	1800
	01:07:39,960 --> 01:07:44,920
	going in a an a Direction that's sort of

	1801
	01:07:43,000 --> 01:07:46,400
	in distribution for the big model like

	1802
	01:07:44,920 --> 01:07:49,240
	something that's within the realm of

	1803
	01:07:46,400 --> 01:07:50,720
	what it might SLE and to there's sort of

	1804
	01:07:49,240 --> 01:07:52,400
	an involved discussion in this paper of

	1805
	01:07:50,720 --> 01:07:55,200
	how you determine if something is in

	1806
	01:07:52,400 --> 01:07:58,000
	distribution um so here the smaller

	1807
	01:07:55,200 --> 01:08:00,240
	models generates like five or six tokens

	1808
	01:07:58,000 --> 01:08:02,559
	that the larger model says okay this

	1809
	01:08:00,240 --> 01:08:03,680
	looks great until it hits a token that

	1810
	01:08:02,559 --> 01:08:06,079
	the larger model would not have

	1811
	01:08:03,680 --> 01:08:07,920
	generated in that circumstance and then

	1812
	01:08:06,079 --> 01:08:10,279
	the larger model rejects that token and

	1813
	01:08:07,920 --> 01:08:13,000
	generates a different token instead so

	1814
	01:08:10,279 --> 01:08:15,440
	you can see here each of these red and

	1815
	01:08:13,000 --> 01:08:17,600
	then blue sections is where the larger

	1816
	01:08:15,440 --> 01:08:19,400
	model has rejected something and has to

	1817
	01:08:17,600 --> 01:08:21,920
	actually autor regressively decode a

	1818
	01:08:19,400 --> 01:08:24,199
	single token by contrast if you were

	1819
	01:08:21,920 --> 01:08:27,359
	doing regular decoding at each

	1820
	01:08:24,199 --> 01:08:28,799
	individual token in this sequence the um

	1821
	01:08:27,359 --> 01:08:31,640
	larger model would have had to make the

	1822
	01:08:28,799 --> 01:08:35,359
	fall forward pass to decoda token so

	1823
	01:08:31,640 --> 01:08:37,359
	here rather than de doing maybe what

	1824
	01:08:35,359 --> 01:08:39,239
	probably like 20ish decoding steps to

	1825
	01:08:37,359 --> 01:08:41,560
	get this full sequence the larger model

	1826
	01:08:39,239 --> 01:08:43,040
	has done about eight decoring steps and

	1827
	01:08:41,560 --> 01:08:47,560
	everything else is able to sort of

	1828
	01:08:43,040 --> 01:08:49,759
	verify a block of tokens at once um this

	1829
	01:08:47,560 --> 01:08:51,400
	sort of idea of like using a smaller

	1830
	01:08:49,759 --> 01:08:54,120
	model as an approximation is pretty

	1831
	01:08:51,400 --> 01:08:55,839
	powerful um and there's some great um

	1832
	01:08:54,120 --> 01:08:58,159
	followup work cons specul decoding and

	1833
	01:08:55,839 --> 01:08:59,000
	sort of ways to do this faster or with

	1834
	01:08:58,159 --> 01:09:01,520
	stronger

	1835
	01:08:59,000 --> 01:09:04,839
	guarantees um but this General concept

	1836
	01:09:01,520 --> 01:09:06,920
	is I would bet probably how models like

	1837
	01:09:04,839 --> 01:09:09,080
	um part of how models like chat GPT or

	1838
	01:09:06,920 --> 01:09:11,159
	Bard are sort of generating text so

	1839
	01:09:09,080 --> 01:09:13,120
	quickly um there's another element here

	1840
	01:09:11,159 --> 01:09:16,159
	which is like the model architecture

	1841
	01:09:13,120 --> 01:09:17,679
	being sparse but I think that um if you

	1842
	01:09:16,159 --> 01:09:19,920
	folks talk about mixture of experts we

	1843
	01:09:17,679 --> 01:09:22,880
	might get into that

	1844
	01:09:19,920 --> 01:09:26,080
	later um how do you do this kind of fast

	1845
	01:09:22,880 --> 01:09:27,679
	inference um libraries like BLM will

	1846
	01:09:26,080 --> 01:09:29,440
	Implement things I think Implement

	1847
	01:09:27,679 --> 01:09:32,199
	speculative decoding and Implement sort

	1848
	01:09:29,440 --> 01:09:34,400
	of Hardware level tricks like choosing

	1849
	01:09:32,199 --> 01:09:37,799
	which attention um weights to Cash wear

	1850
	01:09:34,400 --> 01:09:39,199
	to do faster inflence um there's also

	1851
	01:09:37,799 --> 01:09:40,799
	great libraries for doing things like

	1852
	01:09:39,199 --> 01:09:42,679
	constraint decoding so things like

	1853
	01:09:40,799 --> 01:09:45,520
	outlines will let you set constraints

	1854
	01:09:42,679 --> 01:09:46,960
	like I want my outputs to all be Json

	1855
	01:09:45,520 --> 01:09:48,640
	and it will impose additional

	1856
	01:09:46,960 --> 01:09:50,839
	constraints during decoding to ensure

	1857
	01:09:48,640 --> 01:09:52,279
	that that happens and then pretty much

	1858
	01:09:50,839 --> 01:09:53,960
	anything in these first couple of

	1859
	01:09:52,279 --> 01:09:56,560
	sections we talked about um like

	1860
	01:09:53,960 --> 01:09:58,440
	sampling mode seeking search and

	1861
	01:09:56,560 --> 01:10:00,400
	sometimes MBR will also be implemented

	1862
	01:09:58,440 --> 01:10:05,080
	in pretty much any Library you use for

	1863
	01:10:00,400 --> 01:10:07,679
	models like huggingface Fair seek or

	1864
	01:10:05,080 --> 01:10:10,000
	Jacks so to kind of take a step back

	1865
	01:10:07,679 --> 01:10:12,520
	here is when you get to the end of class

	1866
	01:10:10,000 --> 01:10:15,640
	um there's really two broad categories

	1867
	01:10:12,520 --> 01:10:17,679
	of methods that we talked about today um

	1868
	01:10:15,640 --> 01:10:20,360
	given our initial distribution from the

	1869
	01:10:17,679 --> 01:10:22,600
	model for a next token given our our

	1870
	01:10:20,360 --> 01:10:24,920
	input we can do two kind of different

	1871
	01:10:22,600 --> 01:10:26,400
	things we can each individual decoding

	1872
	01:10:24,920 --> 01:10:28,360
	step choose some kind of function to

	1873
	01:10:26,400 --> 01:10:30,280
	manipulate this distribution and this

	1874
	01:10:28,360 --> 01:10:32,280
	could be something like short like

	1875
	01:10:30,280 --> 01:10:33,960
	cutting off the long tail like modifying

	1876
	01:10:32,280 --> 01:10:36,239
	the temperature or adding external

	1877
	01:10:33,960 --> 01:10:38,400
	information from another model or from a

	1878
	01:10:36,239 --> 01:10:41,480
	discriminator model

	1879
	01:10:38,400 --> 01:10:43,159
	right or we can over a larger part of

	1880
	01:10:41,480 --> 01:10:45,120
	the decoding process choose some

	1881
	01:10:43,159 --> 01:10:47,120
	function to choose between sequences and

	1882
	01:10:45,120 --> 01:10:49,199
	this could be like choosing between next

	1883
	01:10:47,120 --> 01:10:51,679
	tokens in beam search when we pruning

	1884
	01:10:49,199 --> 01:10:53,120
	beams this could be choosing from Full

	1885
	01:10:51,679 --> 01:10:56,760
	sequences when we're doing something

	1886
	01:10:53,120 --> 01:10:58,040
	like MB r or sample and rerank methods

	1887
	01:10:56,760 --> 01:11:00,239
	um and you can do these two things in

	1888
	01:10:58,040 --> 01:11:01,440
	parallel right you can choose like a

	1889
	01:11:00,239 --> 01:11:03,159
	different function to manipulate the

	1890
	01:11:01,440 --> 01:11:04,760
	next token distribution and then some

	1891
	01:11:03,159 --> 01:11:06,199
	sort of like broader thing to choose

	1892
	01:11:04,760 --> 01:11:08,280
	what you do with the full sequences you

	1893
	01:11:06,199 --> 01:11:09,920
	get out of that distribution um but

	1894
	01:11:08,280 --> 01:11:12,040
	there are sort of these two broad

	1895
	01:11:09,920 --> 01:11:14,880
	categories of

	1896
	01:11:12,040 --> 01:11:17,440
	decoding so what should you take away

	1897
	01:11:14,880 --> 01:11:19,400
	from this um I think a couple of things

	1898
	01:11:17,440 --> 01:11:21,000
	you decoding methods can be really

	1899
	01:11:19,400 --> 01:11:23,040
	powerful to control features of your

	1900
	01:11:21,000 --> 01:11:25,040
	output if you want to impose particular

	1901
	01:11:23,040 --> 01:11:26,679
	constraints if you want to factor in

	1902
	01:11:25,040 --> 01:11:27,960
	reward function or factor in a data

	1903
	01:11:26,679 --> 01:11:31,800
	source that you maybe didn't have at

	1904
	01:11:27,960 --> 01:11:34,239
	training time um and to some extent you

	1905
	01:11:31,800 --> 01:11:36,120
	can do a more expensive decoding method

	1906
	01:11:34,239 --> 01:11:37,520
	to compensate for a worse model or to

	1907
	01:11:36,120 --> 01:11:39,080
	compensate for a model that hasn't been

	1908
	01:11:37,520 --> 01:11:42,480
	trained to do exactly the thing you want

	1909
	01:11:39,080 --> 01:11:44,800
	it to do um of course you can't you know

	1910
	01:11:42,480 --> 01:11:47,679
	use this to make gpt2 small as good as

	1911
	01:11:44,800 --> 01:11:49,840
	gp4 but you can sort of for some points

	1912
	01:11:47,679 --> 01:11:51,679
	in the middle spend more um computed

	1913
	01:11:49,840 --> 01:11:53,159
	inference time to pay for not spending

	1914
	01:11:51,679 --> 01:11:55,639
	as much computed training time and

	1915
	01:11:53,159 --> 01:11:57,440
	particularly if you don't have access to

	1916
	01:11:55,639 --> 01:11:59,400
	the kind of giant gpus you might need to

	1917
	01:11:57,440 --> 01:12:01,840
	continue fine-tuning your model this can

	1918
	01:11:59,400 --> 01:12:05,679
	be a really a really powerful

	1919
	01:12:01,840 --> 01:12:07,800
	alternative um yeah so say like you're

	1920
	01:12:05,679 --> 01:12:12,560
	building like something in production

	1921
	01:12:07,800 --> 01:12:15,920
	right people usually do um sort of like

	1922
	01:12:12,560 --> 01:12:18,760
	that you know inance before cling to see

	1923
	01:12:15,920 --> 01:12:21,840
	if it's G to work at do

	1924
	01:12:18,760 --> 01:12:25,080
	that like try to see like if you have a

	1925
	01:12:21,840 --> 01:12:26,800
	model that you can do some kind of

	1926
	01:12:25,080 --> 01:12:29,199
	expensive decoding method for to get

	1927
	01:12:26,800 --> 01:12:31,120
	good outputs is it then worth try

	1928
	01:12:29,199 --> 01:12:34,000
	training that model right um there's

	1929
	01:12:31,120 --> 01:12:36,560
	some great recent work on like training

	1930
	01:12:34,000 --> 01:12:39,400
	models to produce the same kind of

	1931
	01:12:36,560 --> 01:12:40,760
	outputs you get out of MVR without um

	1932
	01:12:39,400 --> 01:12:43,239
	actually doing a really expensive

	1933
	01:12:40,760 --> 01:12:45,600
	inference Stu so at some level like yeah

	1934
	01:12:43,239 --> 01:12:48,120
	you can decide like this model is good

	1935
	01:12:45,600 --> 01:12:49,920
	enough with its expensive method we can

	1936
	01:12:48,120 --> 01:12:50,920
	try to make it cheaper by spending more

	1937
	01:12:49,920 --> 01:12:53,960
	money on

	1938
	01:12:50,920 --> 01:12:55,520
	funing um but that's not it's not like

	1939
	01:12:53,960 --> 01:12:57,320
	necessarily guaranteed that that's will

	1940
	01:12:55,520 --> 01:13:00,679
	be the case

	1941
	01:12:57,320 --> 01:13:03,040
	Okay um the methods that we looked at

	1942
	01:13:00,679 --> 01:13:06,199
	have these sort of trade-offs in quality

	1943
	01:13:03,040 --> 01:13:07,960
	in diversity and in inference speed so

	1944
	01:13:06,199 --> 01:13:10,320
	sampling from your model directly is

	1945
	01:13:07,960 --> 01:13:13,120
	pretty fast to do you get really diverse

	1946
	01:13:10,320 --> 01:13:14,960
	outputs but it tends to be lower quality

	1947
	01:13:13,120 --> 01:13:16,320
	um whereas more restricted sampling

	1948
	01:13:14,960 --> 01:13:18,520
	these sort of mode seeking search

	1949
	01:13:16,320 --> 01:13:20,639
	methods tend to be higher quality but

	1950
	01:13:18,520 --> 01:13:21,880
	you get less less diverse outputs and

	1951
	01:13:20,639 --> 01:13:23,560
	that's why we have these methods like

	1952
	01:13:21,880 --> 01:13:26,719
	diverse and stochastic resarch to

	1953
	01:13:23,560 --> 01:13:28,760
	counter this a bit um and then methods

	1954
	01:13:26,719 --> 01:13:30,400
	like MBR or other sample and rerank

	1955
	01:13:28,760 --> 01:13:32,679
	methods tend to be very high quality

	1956
	01:13:30,400 --> 01:13:34,280
	outputs but you pay for this with much

	1957
	01:13:32,679 --> 01:13:36,520
	slower inference

	1958
	01:13:34,280 --> 01:13:38,679
	time um but if I can kind of convince

	1959
	01:13:36,520 --> 01:13:41,560
	you of anything today I think it would

	1960
	01:13:38,679 --> 01:13:43,600
	be this which is that these the decoding

	1961
	01:13:41,560 --> 01:13:45,600
	method you choose for your model has a

	1962
	01:13:43,600 --> 01:13:47,960
	really strong impact on performance

	1963
	01:13:45,600 --> 01:13:49,520
	Downstream um you can get radically

	1964
	01:13:47,960 --> 01:13:51,239
	different results out of the same model

	1965
	01:13:49,520 --> 01:13:52,639
	without doing any additional training

	1966
	01:13:51,239 --> 01:13:55,120
	just by choosing the different decoding

	1967
	01:13:52,639 --> 01:13:57,880
	method that you might want to try and so

	1968
	01:13:55,120 --> 01:13:59,679
	when you sort of let your libraries pick

	1969
	01:13:57,880 --> 01:14:01,159
	a quote unquote like sensible default

	1970
	01:13:59,679 --> 01:14:03,760
	you can leave a lot of performance on

	1971
	01:14:01,159 --> 01:14:06,480
	the train on the table so I encourage

	1972
	01:14:03,760 --> 01:14:08,199
	you folks that if if you're um deploying

	1973
	01:14:06,480 --> 01:14:09,760
	models in production or if you're doing

	1974
	01:14:08,199 --> 01:14:10,840
	research or you know maybe look at your

	1975
	01:14:09,760 --> 01:14:13,280
	outputs and your model has some

	1976
	01:14:10,840 --> 01:14:15,320
	undesirable behaviors to consider if the

	1977
	01:14:13,280 --> 01:14:17,800
	decoding method you're using is imposing

	1978
	01:14:15,320 --> 01:14:20,000
	some kind of Intuition or some kind of

	1979
	01:14:17,800 --> 01:14:21,840
	inductive bias and if you can alter that

	1980
	01:14:20,000 --> 01:14:24,239
	to get some of these behaviors without

	1981
	01:14:21,840 --> 01:14:26,320
	resorting to additional training

	1982
	01:14:24,239 --> 01:14:28,719
	um and that's sort of the end I can take

	1983
	01:14:26,320 --> 01:14:28,719
	any other

	1984
	01:14:34,320 --> 01:14:38,719
	questions okay um yeah I guess we don't

	1985
	01:14:37,199 --> 01:14:41,360
	have any questions we can take questions

	1986
	01:14:38,719 --> 01:14:45,560
	up here um one one thing I'd like to

	1987
	01:14:41,360 --> 01:14:47,679
	point out also is that um I I love the

	1988
	01:14:45,560 --> 01:14:50,760
	final thing that Amanda said here

	1989
	01:14:47,679 --> 01:14:54,199
	another thing is that my impression from

	1990
	01:14:50,760 --> 01:14:56,400
	dealing with things is that it's a lot

	1991
	01:14:54,199 --> 01:14:58,159
	easier to predict the effect of

	1992
	01:14:56,400 --> 01:14:59,920
	inference time decoding time

	1993
	01:14:58,159 --> 01:15:01,120
	manipulations than it is to predict the

	1994
	01:14:59,920 --> 01:15:04,239
	effect of

	1995
	01:15:01,120 --> 01:15:07,480
	like um fine-tuning or something like

	1996
	01:15:04,239 --> 01:15:11,040
	this like just to give a an

	1997
	01:15:07,480 --> 01:15:12,480
	example beam search with the maximum

	1998
	01:15:11,040 --> 01:15:15,199
	likelihood trained model tends to

	1999
	01:15:12,480 --> 01:15:16,719
	generate things that are shorter um

	2000
	01:15:15,199 --> 01:15:18,040
	whereas greedy decoding tends to

	2001
	01:15:16,719 --> 01:15:19,639
	generate things that are longer and

	2002
	01:15:18,040 --> 01:15:22,000
	repeat more often and stuff like that

	2003
	01:15:19,639 --> 01:15:25,920
	and if you try a few methods like this

	2004
	01:15:22,000 --> 01:15:28,920
	you'll quickly find these kind of qus of

	2005
	01:15:25,920 --> 01:15:31,320
	each of the methods and so by forming a

	2006
	01:15:28,920 --> 01:15:32,719
	good intuition of this you will also

	2007
	01:15:31,320 --> 01:15:34,000
	know how to fix these problems when you

	2008
	01:15:32,719 --> 01:15:35,600
	see them it's like oh my model's

	2009
	01:15:34,000 --> 01:15:37,320
	repeating itself a lot maybe I shouldn't

	2010
	01:15:35,600 --> 01:15:38,679
	be using grey search I should be

	2011
	01:15:37,320 --> 01:15:41,199
	switching over to something else or

	2012
	01:15:38,679 --> 01:15:43,320
	something like that so um this is a good

	2013
	01:15:41,199 --> 01:15:45,880
	thing to know and play around with yeah

	2014
	01:15:43,320 --> 01:15:47,239
	and I think pretty underutilized too um

	2015
	01:15:45,880 --> 01:15:48,880
	a lot of folks will not think about a

	2016
	01:15:47,239 --> 01:15:50,920
	decoding method to fix their problem

	2017
	01:15:48,880 --> 01:15:52,280
	even if like your model might actually

	2018
	01:15:50,920 --> 01:15:53,760
	be perfectly fine under a different

	2019
	01:15:52,280 --> 01:15:56,000
	decoding strategy

	2020
	01:15:53,760 --> 01:15:58,320
	great okay thanks a lot everyone you can

	2021
	01:15:56,000 --> 01:15:58,320
	uh

	2022
	01:16:02,280 --> 01:16:05,280
	finish