Datasets:

ahmedelsayed
/

videos

	1
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	so uh I guess we can get started uh

	2
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	today I'm going to be talking about code

	3
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	generation and uh so this is a a

	4
	00:00:09,880 --> 00:00:13,599
	research topic that I've uh worked on

	5
	00:00:11,719 --> 00:00:15,280
	for a long time now I I like a lot it's

	6
	00:00:13,599 --> 00:00:17,520
	become very useful nowadays which is

	7
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	very exciting um so I'd like to talk

	8
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	about kind of some of the basics and

	9
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	Frontiers uh that we're working on right

	10
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	now in this General uh area

	11
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	um

	12
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	so before I get into code generation

	13
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	specifically one thing I'd like to point

	14
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	out is for the next four or so classes

	15
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	I'm going to be talking about tasks and

	16
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	up until now I've been focusing on a lot

	17
	00:00:45,680 --> 00:00:52,840
	of like General things that weren't as

	18
	00:00:48,680 --> 00:00:55,199
	much about any specific tasks um

	19
	00:00:52,840 --> 00:00:57,000
	and I know that not everybody's going to

	20
	00:00:55,199 --> 00:00:59,399
	be interested in the four tasks that I'm

	21
	00:00:57,000 --> 00:01:00,960
	talking about in the next you know four

	22
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	lectures

	23
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	um

	24
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	but I'm going to be covering various

	25
	00:01:04,920 --> 00:01:08,680
	things about different tasks and

	26
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	hopefully you can map the same questions

	27
	00:01:08,680 --> 00:01:12,040
	onto whatever task you are interested in

	28
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	if you're not interested in any of the

	29
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	ones I talk about here so basically what

	30
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	I want to talk about is the task

	31
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	objective like why do we do that task

	32
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	why is it important um what data sets

	33
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	can we use to train or test our models

	34
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	on these tasks evaluation metrics and

	35
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	how do we evaluate uh both manually and

	36
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	automatically with respect to how good

	37
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	we're

	38
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	doing and finally models and methods so

	39
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	you know how do we solve the

	40
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	problem and so for code generation first

	41
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	I'd like to talk about the overview and

	42
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	objectives of code generation so

	43
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	basically code generation is the task of

	44
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	generating executable code is an

	45
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	interface to uh a program or to

	46
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	computers and there's a lot of different

	47
	00:01:54,479 --> 00:02:01,000
	ways we can do this um why do we want to

	48
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	do this so

	49
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	the first thing is that software

	50
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	engineering is really important and

	51
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	being able to generate code accelerate

	52
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	software engineering uh now code

	53
	00:02:09,640 --> 00:02:13,640
	generation is practical and I hope that

	54
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	everybody in the class is using some

	55
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	sort of you know code generation to

	56
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	accelerate your own workflow if you're

	57
	00:02:17,840 --> 00:02:22,599
	not I highly encourage you to to try it

	58
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	because it's very

	59
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	useful second it also does things like

	60
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	enabling models to access tools um

	61
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	and even if you're not specifically

	62
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	working on a software related task this

	63
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	can be helpful but I want to talk about

	64
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	this in a later class when we talk about

	65
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	llm agents so I'm not going to be

	66
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	talking about um that as much this time

	67
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	uh one other thing that I I forgot to

	68
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	mention here which I'm also going to

	69
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	talk about in the later class is even if

	70
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	you're not using code at all training on

	71
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	code has been shown to cause some

	72
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	benefits to learning models uh

	73
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	specifically with respect to learning

	74
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	like difficult multitask reasoning uh

	75
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	sorry multi-step reasoning tasks and so

	76
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	that's another reason why you might want

	77
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	to worry about codes so I'm going to

	78
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	mainly talk about the first one this

	79
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	time and leave the other two uh for

	80
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	future

	81
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	lectures so specifically for this task

	82
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	our input um is some sort of

	83
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	specification of what we want to do um

	84
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	and our output is going to be

	85
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	code so

	86
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	when you write a

	87
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	program how do you describe the thing

	88
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	that you want to implement in the

	89
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	program before you implement it like uh

	90
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	yeah what are some of the specifications

	91
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	that people can give

	92
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	you what the input and output of the

	93
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	functions are uh yes uh sorry what what

	94
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	types the inputs and outputs of the

	95
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	function are so those would be like type

	96
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	in in Python for example yeah that

	97
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	that's a good one it's actually not on

	98
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	my list of things here but it's it's a

	99
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	good Point yeah any any other things

	100
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	yeah complexity requirements complexity

	101
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	requirements constraints that is also

	102
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	not on my list of things here uh that's

	103
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	uh that's a good one too um and any uh

	104
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	slightly more straight forward

	105
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	things pseudo code yeah um in pseudo

	106
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	code uh what what is pseudo code written

	107
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	in natural natural language yeah so

	108
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	natural language inputs are are one

	109
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	thing so I will tell you I want I want a

	110
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	program that uh I want you to write a

	111
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	web interface that allows me to um order

	112
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	pizza or something like that that that

	113
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	would be one way to do it any other

	114
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	ideas

	115
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	yeah this is what I have and this is

	116
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	what I want yeah so um that's especially

	117
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	the case if you're like modifying a

	118
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	program um or something like that so

	119
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	actually the next one on my list there

	120
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	so good good point um any other

	121
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	ideas yeah or or a multimodal person you

	122
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	know I might say I want a pizza ordering

	123
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	I want a pizza ordering app and up here

	124
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	it should have your like username so you

	125
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	can click through the settings and like

	126
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	over here you should have the menu and

	127
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	over here you should have your check out

	128
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	card or something like that you know

	129
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	it's something you do for a programmer

	130
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	as well until recently we couldn't

	131
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	really use that with like actual models

	132
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	but um yeah yeah well that was my fourth

	133
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	one but um and then the other one uh

	134
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	inputs and outputs this could come in

	135
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	the form of like unit tests or something

	136
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	like that where it's like yeah this is

	137
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	the input this is the expected output so

	138
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	these are all things we use both as

	139
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	human programmers and in code generation

	140
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	models I really like the two other

	141
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	points though um

	142
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	because typin

	143
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	are actually something that you like

	144
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	writing writing with typ pints is

	145
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	actually something that you can do with

	146
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	code generation models and um

	147
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	constraints such as like it should it

	148
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	should meet certain speed requirements

	149
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	or it should um you know use certain

	150
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	libraries or something like that are

	151
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	also constraints that you could add I

	152
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	didn't put that on this slide here that

	153
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	might come in the natural language

	154
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	description but it could be something

	155
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	separate and then you know the output is

	156
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	whatever code you want

	157
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	to so um how many people are using like

	158
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	GitHub

	159
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	co-pilot like what

	160
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	percentage maybe about half okay um how

	161
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	many people are using another like

	162
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	assisted coding tool other than GitHub

	163
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	coet yeah g gp4 gp4 is an could be an

	164
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	assisted coding tool I'm talking more

	165
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	like something that's actually in your

	166
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	IDE something yeah anybody

	167
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	else does anyone use

	168
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	cursor no

	169
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	um yeah cursor yeah okay so

	170
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	yeah Co collab uh Ai and collab yeah so

	171
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	um so I think there are a lot of these

	172
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	uh going around I I use co-pilot myself

	173
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	I have not used cursor I do use GPD 4 um

	174
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	and I'll I'll show you an example of how

	175
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	I use them different

	176
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	um if you haven't used copilot hopefully

	177
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	this will

	178
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	work um I just made a a simple

	179
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	video

	180
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	oops okay that's not working but anyway

	181
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	you um you type your uh you know you

	182
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	type and it basically completes your

	183
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	code so this is this is an example here

	184
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	and I didn't write any of this code

	185
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	actually I just wrote the comments and

	186
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	then it filled in the the actual C and

	187
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	also I didn't exactly check if it's

	188
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	correct or not

	189
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	so if there's any mistake it's co

	190
	00:08:08,080 --> 00:08:15,159
	Pilot's fault not my fault but um I it

	191
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	looked correct to me so

	192
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	um and oh by the way you get to use it

	193
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	for free with your CMU account so if you

	194
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	uh if you don't want to use it but don't

	195
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	want to pay for it you're and left

	196
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	because you can use

	197
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	it um another example uh is gd4 or uh

	198
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	more recently Cloud 3 um and basically

	199
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	this can do a different variety of

	200
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	things so we talked about screenshots

	201
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	and basically I asked Claude to create a

	202
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	react app that replicates the claw

	203
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	interface by giving it a screenshot and

	204
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	asking it create a react app that looks

	205
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	like the screenshot and then it gave me

	206
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	a whole bunch of text and in the end it

	207
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	started um making this uh container here

	208
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	um

	209
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	and this uh it basically is skipping

	210
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	some of the styling stuff uh because

	211
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	large language models I I think they're

	212
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	basically trained so that they don't

	213
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	give really really long responses

	214
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	because like if you uh asked for

	215
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	something that would take a really

	216
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	really long time and then the model just

	217
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	complied and gave that to you for a

	218
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	really really long time it would cost

	219
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	them a lot of money so I feel like they

	220
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	they B try to train the models to only

	221
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	out at like a thousand tokens at a time

	222
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	or something like that so um it it won't

	223
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	actually go out and program the whole

	224
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	project for you but with a little

	225
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	cajoling if you say okay now implement

	226
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	this part now implement this part now

	227
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	implement this part um you uh you can

	228
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	end up with some pretty interesting

	229
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	stuff and let me

	230
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	uh let me see if I can I can show you an

	231
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	example

	232
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	so I I know a little bit of

	233
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	react um the front end framework but I

	234
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	don't know a whole lot but recently

	235
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	we've been um working on an open-source

	236
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	assisted coding app and I most of this

	237
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	was just written by quad um it's uh I I

	238
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	said I want an app that on the left side

	239
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	it has a chat window and then on the

	240
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	right side it has three uh three panes

	241
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	one is a terminal one is a planner and

	242
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	one is a code editor

	243
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	and um so it gave me something it was

	244
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	kind of ugly so I said okay make the

	245
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	background black um change the CSS file

	246
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	so that um you have like a user icon and

	247
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	a robot icon and stuff like that and

	248
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	after this I I wrote very little of this

	249
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	code I wrote like 1% of this code or

	250
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	something like that and it's able to to

	251
	00:10:51,079 --> 00:10:57,880
	do these sorts of things for you um so

	252
	00:10:54,480 --> 00:11:01,000
	if you don't like writing front ends

	253
	00:10:57,880 --> 00:11:03,880
	good luck uh or good good news that you

	254
	00:11:01,000 --> 00:11:05,560
	uh can come up with a passable front end

	255
	00:11:03,880 --> 00:11:07,519
	without uh without actually having to

	256
	00:11:05,560 --> 00:11:08,720
	write it nonetheless you know good front

	257
	00:11:07,519 --> 00:11:10,200
	end Engineers will come up with

	258
	00:11:08,720 --> 00:11:13,639
	something much more beautiful than that

	259
	00:11:10,200 --> 00:11:15,880
	so um so basically why do I why did I

	260
	00:11:13,639 --> 00:11:19,959
	want to say this I think um GitHub

	261
	00:11:15,880 --> 00:11:20,839
	co-pilot and Pla or gp4 serve very

	262
	00:11:19,959 --> 00:11:25,200
	different

	263
	00:11:20,839 --> 00:11:27,360
	purposes um GitHub co-pilot is code

	264
	00:11:25,200 --> 00:11:30,160
	completion and it mostly works for

	265
	00:11:27,360 --> 00:11:32,440
	shorter things so it's like your next

	266
	00:11:30,160 --> 00:11:34,760
	thought in your code in code that you

	267
	00:11:32,440 --> 00:11:37,560
	know pretty well something like plot or

	268
	00:11:34,760 --> 00:11:40,639
	gp4 is much better for really long

	269
	00:11:37,560 --> 00:11:44,680
	things um where you want to build like a

	270
	00:11:40,639 --> 00:11:47,040
	full class or something like that and I

	271
	00:11:44,680 --> 00:11:48,480
	also have found that if you're coding in

	272
	00:11:47,040 --> 00:11:50,079
	a language that you're very familiar

	273
	00:11:48,480 --> 00:11:51,560
	with copilot might be more useful

	274
	00:11:50,079 --> 00:11:52,959
	because you want fine grain control and

	275
	00:11:51,560 --> 00:11:55,040
	you want it to fill out things to make

	276
	00:11:52,959 --> 00:11:56,519
	it faster whereas if you're coding in a

	277
	00:11:55,040 --> 00:11:58,040
	language that you're not very familiar

	278
	00:11:56,519 --> 00:11:59,680
	with something like Claud is good

	279
	00:11:58,040 --> 00:12:01,839
	because you can write a whole you know

	280
	00:11:59,680 --> 00:12:04,800
	program forties so these are the

	281
	00:12:01,839 --> 00:12:07,680
	differences another thing is GitHub

	282
	00:12:04,800 --> 00:12:09,240
	co-pilot needs to be frighteningly fast

	283
	00:12:07,680 --> 00:12:10,839
	because it needs to move at the speed

	284
	00:12:09,240 --> 00:12:12,880
	that like programmers are thinking in

	285
	00:12:10,839 --> 00:12:14,920
	programming next whereas something like

	286
	00:12:12,880 --> 00:12:16,800
	Claud it doesn't you know using it in

	287
	00:12:14,920 --> 00:12:18,880
	the way that I use cloud here doesn't

	288
	00:12:16,800 --> 00:12:22,600
	really matter because I can say uh

	289
	00:12:18,880 --> 00:12:24,079
	programing me a you know a web app and

	290
	00:12:22,600 --> 00:12:25,360
	then I can go and have dinner and come

	291
	00:12:24,079 --> 00:12:28,199
	back and have a web app and I'd be

	292
	00:12:25,360 --> 00:12:31,720
	perfectly happy with that right so um

	293
	00:12:28,199 --> 00:12:37,199
	the latency request are also

	294
	00:12:31,720 --> 00:12:37,199
	different cool um any any questions here

	295
	00:12:37,399 --> 00:12:42,600
	yeah that debugging code they

	296
	00:12:43,000 --> 00:12:47,959
	are the well so

	297
	00:12:45,839 --> 00:12:50,760
	co-pilot I haven't actually tried it

	298
	00:12:47,959 --> 00:12:52,480
	that much um if I wanted to debug code

	299
	00:12:50,760 --> 00:12:54,880
	I'd probably use something like pla or

	300
	00:12:52,480 --> 00:12:56,360
	gp4 just because actually I'll I'll

	301
	00:12:54,880 --> 00:12:58,320
	mention this in a second but co-pilot's

	302
	00:12:56,360 --> 00:13:00,360
	a much smaller model uh because it needs

	303
	00:12:58,320 --> 00:13:01,839
	to be very fast or what they're using in

	304
	00:13:00,360 --> 00:13:04,040
	copilot is a smaller model because it

	305
	00:13:01,839 --> 00:13:05,519
	needs to be very fast so I would

	306
	00:13:04,040 --> 00:13:08,360
	probably use a bigger model for anything

	307
	00:13:05,519 --> 00:13:10,120
	that required like good understanding I

	308
	00:13:08,360 --> 00:13:11,480
	think it's passable at debugging code

	309
	00:13:10,120 --> 00:13:13,079
	but it won't find the really difficult

	310
	00:13:11,480 --> 00:13:15,639
	things and it probably won't find things

	311
	00:13:13,079 --> 00:13:18,279
	that require spanning across uh multiple

	312
	00:13:15,639 --> 00:13:21,240
	files but I I'm not 100% sure about that

	313
	00:13:18,279 --> 00:13:25,519
	like I think it's worth

	314
	00:13:21,240 --> 00:13:25,519
	testing um any other

	315
	00:13:25,880 --> 00:13:30,120
	questions okay so if I haven't convinced

	316
	00:13:28,360 --> 00:13:32,360
	you that as software developers you

	317
	00:13:30,120 --> 00:13:34,880
	should be using this hopefully this next

	318
	00:13:32,360 --> 00:13:37,480
	uh this next slide will so this was a

	319
	00:13:34,880 --> 00:13:41,199
	study that was run by GitHub uh shortly

	320
	00:13:37,480 --> 00:13:43,160
	after um after co-pilot came out and so

	321
	00:13:41,199 --> 00:13:45,440
	why do we do code generation why are

	322
	00:13:43,160 --> 00:13:47,240
	people very excited about it so the

	323
	00:13:45,440 --> 00:13:50,240
	first is U making software isn't

	324
	00:13:47,240 --> 00:13:53,480
	important um and I recently calculated

	325
	00:13:50,240 --> 00:13:55,920
	what from some Labor Statistics and the

	326
	00:13:53,480 --> 00:13:59,440
	total amount that software developers

	327
	00:13:55,920 --> 00:14:01,880
	make um in a year is $175 billion so

	328
	00:13:59,440 --> 00:14:05,000
	that's providing at least that much you

	329
	00:14:01,880 --> 00:14:06,800
	know value so it's a very high value uh

	330
	00:14:05,000 --> 00:14:09,079
	profession so if we could make it faster

	331
	00:14:06,800 --> 00:14:11,480
	you know it would have even more

	332
	00:14:09,079 --> 00:14:12,920
	value another thing is code generation

	333
	00:14:11,480 --> 00:14:15,680
	leads to large improvements in

	334
	00:14:12,920 --> 00:14:17,160
	productivity so uh get Hub ran this

	335
	00:14:15,680 --> 00:14:18,680
	study where they randomly assigned

	336
	00:14:17,160 --> 00:14:21,519
	developers to groups who would either

	337
	00:14:18,680 --> 00:14:24,440
	use co-pilot or not use co-pilot and

	338
	00:14:21,519 --> 00:14:26,480
	they assigned them the same task and

	339
	00:14:24,440 --> 00:14:30,759
	basically the people who use copilot

	340
	00:14:26,480 --> 00:14:34,199
	their rate of um completion went up by

	341
	00:14:30,759 --> 00:14:36,320
	8% and they finished um in about 40% of

	342
	00:14:34,199 --> 00:14:39,279
	the time of the people who didn't use it

	343
	00:14:36,320 --> 00:14:43,639
	and so I think this

	344
	00:14:39,279 --> 00:14:45,920
	is or uh yeah they say 55% less times so

	345
	00:14:43,639 --> 00:14:47,759
	this is very impressive but it's also

	346
	00:14:45,920 --> 00:14:50,199
	not at all surprising if you're using a

	347
	00:14:47,759 --> 00:14:52,880
	Cod like assisted coding assistant it

	348
	00:14:50,199 --> 00:14:54,360
	just makes you code faster also if you

	349
	00:14:52,880 --> 00:14:56,040
	don't like writing doc strings it's

	350
	00:14:54,360 --> 00:14:57,519
	really good at writing doc strings so

	351
	00:14:56,040 --> 00:14:59,680
	you can write documentation for your

	352
	00:14:57,519 --> 00:15:00,759
	code not wor about so

	353
	00:14:59,680 --> 00:15:04,399
	okay

	354
	00:15:00,759 --> 00:15:07,000
	cool um

	355
	00:15:04,399 --> 00:15:09,720
	so there are differences between code

	356
	00:15:07,000 --> 00:15:14,000
	and natural language uh and I've listed

	357
	00:15:09,720 --> 00:15:15,560
	a few of them here and the differences

	358
	00:15:14,000 --> 00:15:18,120
	between code and natural language also

	359
	00:15:15,560 --> 00:15:20,160
	affect how we build models for this test

	360
	00:15:18,120 --> 00:15:23,160
	so the first one is that code has strict

	361
	00:15:20,160 --> 00:15:26,000
	grammar uh if you make a small mistake

	362
	00:15:23,160 --> 00:15:27,920
	in your code grammar usually it will

	363
	00:15:26,000 --> 00:15:29,839
	just break and your program won't work

	364
	00:15:27,920 --> 00:15:31,319
	so you need to be very careful as

	365
	00:15:29,839 --> 00:15:32,560
	opposed to natural language grammar

	366
	00:15:31,319 --> 00:15:33,600
	where you can make small mistakes and it

	367
	00:15:32,560 --> 00:15:36,120
	doesn't make a

	368
	00:15:33,600 --> 00:15:40,120
	difference another thing is in code you

	369
	00:15:36,120 --> 00:15:42,720
	know the semantic flow of the code and

	370
	00:15:40,120 --> 00:15:44,160
	so we know that certain variables

	371
	00:15:42,720 --> 00:15:45,560
	correspond to each other we know that

	372
	00:15:44,160 --> 00:15:48,639
	they're flowing through the program in a

	373
	00:15:45,560 --> 00:15:50,880
	certain way another thing is code is

	374
	00:15:48,639 --> 00:15:54,120
	executable so we can actually execute it

	375
	00:15:50,880 --> 00:15:56,199
	and observe the result unlike in natural

	376
	00:15:54,120 --> 00:16:00,000
	language and another important thing is

	377
	00:15:56,199 --> 00:16:03,399
	code is created incrementally so code is

	378
	00:16:00,000 --> 00:16:05,680
	not you know unlike text text is also

	379
	00:16:03,399 --> 00:16:07,399
	created incrementally but it's not

	380
	00:16:05,680 --> 00:16:08,720
	usually you write it once you might

	381
	00:16:07,399 --> 00:16:11,199
	revise it a little bit and then you're

	382
	00:16:08,720 --> 00:16:14,040
	done and you you don't need to touch it

	383
	00:16:11,199 --> 00:16:15,399
	again but um in code you touch it over

	384
	00:16:14,040 --> 00:16:17,800
	and over and over again as you develop a

	385
	00:16:15,399 --> 00:16:17,800
	sof

	386
	00:16:18,040 --> 00:16:23,040
	project so if we look at code Generation

	387
	00:16:21,079 --> 00:16:27,079
	Um I would like to talk a little bit

	388
	00:16:23,040 --> 00:16:29,079
	about uh subtasks and data sets next so

	389
	00:16:27,079 --> 00:16:30,480
	the most famous data set for a Cod code

	390
	00:16:29,079 --> 00:16:34,279
	generation nowadays is something called

	391
	00:16:30,480 --> 00:16:38,680
	human ofel um this is a very nice data

	392
	00:16:34,279 --> 00:16:42,480
	set um for a number of reasons uh I

	393
	00:16:38,680 --> 00:16:44,240
	think it is used too much um nonetheless

	394
	00:16:42,480 --> 00:16:46,759
	and I I think there are better data sets

	395
	00:16:44,240 --> 00:16:51,240
	that we maybe should be using more but

	396
	00:16:46,759 --> 00:16:54,000
	basically human ofel is um it has

	397
	00:16:51,240 --> 00:16:55,920
	examples of usage of the Python standard

	398
	00:16:54,000 --> 00:16:59,360
	Library where some are easier some are

	399
	00:16:55,920 --> 00:17:02,880
	harder and just to give some examples

	400
	00:16:59,360 --> 00:17:06,760
	uh we're saying given a nonempty list of

	401
	00:17:02,880 --> 00:17:10,480
	integers return the sum of all the odd

	402
	00:17:06,760 --> 00:17:12,959
	elements that are in even positions so

	403
	00:17:10,480 --> 00:17:16,079
	it's kind of like a elite code

	404
	00:17:12,959 --> 00:17:19,199
	style you know program but maybe one of

	405
	00:17:16,079 --> 00:17:22,400
	the easier ones and then in order to

	406
	00:17:19,199 --> 00:17:25,240
	solve that you find all of the put

	407
	00:17:22,400 --> 00:17:28,480
	elements in even positions and then you

	408
	00:17:25,240 --> 00:17:29,679
	only return them if uh the value itself

	409
	00:17:28,480 --> 00:17:32,799
	is

	410
	00:17:29,679 --> 00:17:34,200
	um so like you can do that in a oneliner

	411
	00:17:32,799 --> 00:17:36,600
	but you need to think about it a little

	412
	00:17:34,200 --> 00:17:38,919
	bit um and then you have

	413
	00:17:36,600 --> 00:17:43,120
	more

	414
	00:17:38,919 --> 00:17:43,810
	um returns encoded uh sorry takes an

	415
	00:17:43,120 --> 00:17:46,910
	input

	416
	00:17:43,810 --> 00:17:46,910
	[Music]

	417
	00:17:47,160 --> 00:17:50,919
	string yeah actually sorry this is from

	418
	00:17:49,320 --> 00:17:53,600
	the paper I didn't read it before I copy

	419
	00:17:50,919 --> 00:17:57,080
	pasted it in here but um yeah that's a

	420
	00:17:53,600 --> 00:17:58,880
	decoding one and one one thing about

	421
	00:17:57,080 --> 00:18:02,240
	this uh that's important to know is it

	422
	00:17:58,880 --> 00:18:04,200
	only has 164 examples so it's actually a

	423
	00:18:02,240 --> 00:18:07,600
	relatively small number of

	424
	00:18:04,200 --> 00:18:09,440
	examples um it's also just the python

	425
	00:18:07,600 --> 00:18:11,200
	standard Library so it's not testing

	426
	00:18:09,440 --> 00:18:14,960
	usage of any other

	427
	00:18:11,200 --> 00:18:17,520
	libraries um so these two things

	428
	00:18:14,960 --> 00:18:19,720
	together make it not the most realistic

	429
	00:18:17,520 --> 00:18:21,880
	you know examination of your programming

	430
	00:18:19,720 --> 00:18:23,640
	skills just like leak code is not the

	431
	00:18:21,880 --> 00:18:25,640
	most realistic examination of your

	432
	00:18:23,640 --> 00:18:28,240
	programming skills but you know I don't

	433
	00:18:25,640 --> 00:18:31,720
	know companies use it anyway so maybe

	434
	00:18:28,240 --> 00:18:35,159
	human devel is reasonable but um so then

	435
	00:18:31,720 --> 00:18:37,120
	we go um into the inputs and outputs uh

	436
	00:18:35,159 --> 00:18:40,679
	the inputs and outputs usually include a

	437
	00:18:37,120 --> 00:18:43,440
	doc string um some input and output

	438
	00:18:40,679 --> 00:18:47,640
	examples and then they have tests to

	439
	00:18:43,440 --> 00:18:47,640
	verify the accuracy of your

	440
	00:18:47,880 --> 00:18:52,840
	outputs so the metric that's used to

	441
	00:18:50,559 --> 00:18:58,919
	evaluate these systems is something

	442
	00:18:52,840 --> 00:19:01,400
	called passet K and the basic idea is um

	443
	00:18:58,919 --> 00:19:03,400
	we generate K examples will at least one

	444
	00:19:01,400 --> 00:19:06,960
	of them pass the unit

	445
	00:19:03,400 --> 00:19:10,720
	tests and the idea here is

	446
	00:19:06,960 --> 00:19:13,480
	that if we have models we might want to

	447
	00:19:10,720 --> 00:19:14,960
	generate like well there there's a

	448
	00:19:13,480 --> 00:19:17,480
	couple reasons why we would care about

	449
	00:19:14,960 --> 00:19:19,880
	this pass it one is kind of obvious

	450
	00:19:17,480 --> 00:19:23,200
	because we generate one and then we

	451
	00:19:19,880 --> 00:19:26,480
	measure how um you know how likely it is

	452
	00:19:23,200 --> 00:19:29,280
	to pass unit tests but pass it five why

	453
	00:19:26,480 --> 00:19:30,760
	would we care about passet five well

	454
	00:19:29,280 --> 00:19:32,159
	number one maybe you could show five

	455
	00:19:30,760 --> 00:19:34,240
	programs to a person and they could

	456
	00:19:32,159 --> 00:19:37,039
	choose the one that they like the best

	457
	00:19:34,240 --> 00:19:39,919
	or maybe you could have unit test write

	458
	00:19:37,039 --> 00:19:41,720
	unit tests in advance and then generate

	459
	00:19:39,919 --> 00:19:43,880
	five programs check which one pass the

	460
	00:19:41,720 --> 00:19:45,480
	unit tests and then use the ones only

	461
	00:19:43,880 --> 00:19:48,360
	that pass the unit test or something

	462
	00:19:45,480 --> 00:19:51,000
	like that so there's also some interest

	463
	00:19:48,360 --> 00:19:53,320
	in uh whether you could generate you

	464
	00:19:51,000 --> 00:19:54,600
	know multiple examples and then pick a

	465
	00:19:53,320 --> 00:19:56,919
	good

	466
	00:19:54,600 --> 00:19:59,080
	one there's a little bit of nuance in

	467
	00:19:56,919 --> 00:20:02,120
	how this is actually calculated so

	468
	00:19:59,080 --> 00:20:04,240
	basically um if you generate only K like

	469
	00:20:02,120 --> 00:20:05,960
	if you if you sample only one example

	470
	00:20:04,240 --> 00:20:07,400
	there's a lot of variance in whether you

	471
	00:20:05,960 --> 00:20:10,159
	get it right or not so what they

	472
	00:20:07,400 --> 00:20:13,440
	actually do is they generate like 10

	473
	00:20:10,159 --> 00:20:15,600
	outputs or 200 outputs and then they

	474
	00:20:13,440 --> 00:20:18,159
	calculate the expected number of those

	475
	00:20:15,600 --> 00:20:20,320
	that the expected number of cases where

	476
	00:20:18,159 --> 00:20:23,280
	that would pass by just doing a little

	477
	00:20:20,320 --> 00:20:25,440
	bit of uh like math calculating the

	478
	00:20:23,280 --> 00:20:28,679
	number of combinations where one passes

	479
	00:20:25,440 --> 00:20:30,720
	or one doesn't and here k n is the total

	480
	00:20:28,679 --> 00:20:34,240
	number you generate C is the number of

	481
	00:20:30,720 --> 00:20:36,520
	correct ansers and K is uh your passive

	482
	00:20:34,240 --> 00:20:36,520
	K

	483
	00:20:37,159 --> 00:20:43,360
	value

	484
	00:20:38,919 --> 00:20:46,280
	cool um so any any questions about

	485
	00:20:43,360 --> 00:20:47,880
	these you'll you'll see a bunch of uh

	486
	00:20:46,280 --> 00:20:50,520
	people evaluating on this human ofel

	487
	00:20:47,880 --> 00:20:52,760
	with passive K including all of the you

	488
	00:20:50,520 --> 00:20:57,520
	know new llms that come out it's a very

	489
	00:20:52,760 --> 00:20:57,520
	standard Edge yeah

	490
	00:21:01,760 --> 00:21:06,039
	is yeah that that's a good um question I

	491
	00:21:04,919 --> 00:21:07,840
	think I'm going to cover that a little

	492
	00:21:06,039 --> 00:21:11,039
	bit later but I might as well say it now

	493
	00:21:07,840 --> 00:21:13,640
	so llms

	494
	00:21:11,039 --> 00:21:15,080
	are llms are good at code because they

	495
	00:21:13,640 --> 00:21:16,880
	intentionally include a lot of code

	496
	00:21:15,080 --> 00:21:19,520
	training data in LL training and the

	497
	00:21:16,880 --> 00:21:22,679
	reason for that is twofold um the first

	498
	00:21:19,520 --> 00:21:25,320
	one is that code generation is a huge

	499
	00:21:22,679 --> 00:21:26,960
	application of llms right now and like

	500
	00:21:25,320 --> 00:21:28,679
	if you had an llm that couldn't do code

	501
	00:21:26,960 --> 00:21:32,320
	generation it'd be kind of embarrassing

	502
	00:21:28,679 --> 00:21:33,960
	so um Everybody includes this number two

	503
	00:21:32,320 --> 00:21:36,600
	uh code has been shown to improve kind

	504
	00:21:33,960 --> 00:21:38,080
	of the reasoning abilities of llms and

	505
	00:21:36,600 --> 00:21:41,640
	because of that people include code for

	506
	00:21:38,080 --> 00:21:43,440
	that purpose so yeah um it's not that

	507
	00:21:41,640 --> 00:21:45,600
	LMS are inherently good at code or

	508
	00:21:43,440 --> 00:21:48,840
	anything it's that they have lots of

	509
	00:21:45,600 --> 00:21:51,640
	lots of code TR and I'll I'll explain

	510
	00:21:48,840 --> 00:21:54,279
	exactly how they construct this

	511
	00:21:51,640 --> 00:21:57,200
	St and actually if you remember last

	512
	00:21:54,279 --> 00:21:59,640
	time uh I talked about the pile which

	513
	00:21:57,200 --> 00:22:01,039
	was or not last time but uh when I

	514
	00:21:59,640 --> 00:22:03,159
	talked about the tour of large language

	515
	00:22:01,039 --> 00:22:06,360
	models I talked about the pile and the

	516
	00:22:03,159 --> 00:22:09,799
	pile is almost half toe for

	517
	00:22:06,360 --> 00:22:12,000
	example cool any other

	518
	00:22:09,799 --> 00:22:17,240
	questions

	519
	00:22:12,000 --> 00:22:19,320
	okay so another uh a first Improvement

	520
	00:22:17,240 --> 00:22:22,080
	or at least change that we can make to

	521
	00:22:19,320 --> 00:22:23,880
	human ofel is uh going to broader

	522
	00:22:22,080 --> 00:22:26,720
	domains and covering a broader variety

	523
	00:22:23,880 --> 00:22:28,559
	of libraries and this is a data set that

	524
	00:22:26,720 --> 00:22:30,880
	we created actually a long time ago but

	525
	00:22:28,559 --> 00:22:33,799
	but we recently added execution based

	526
	00:22:30,880 --> 00:22:36,159
	evaluation to it it's called konola and

	527
	00:22:33,799 --> 00:22:36,919
	the execution based uh evaluation one is

	528
	00:22:36,159 --> 00:22:40,360
	called

	529
	00:22:36,919 --> 00:22:43,039
	odex and basically what we did here is

	530
	00:22:40,360 --> 00:22:45,720
	we scraped data from stack Overflow

	531
	00:22:43,039 --> 00:22:48,039
	including uh inputs and output uh

	532
	00:22:45,720 --> 00:22:50,559
	Solutions and then based on this scraped

	533
	00:22:48,039 --> 00:22:54,240
	data we uh did some manual curation to

	534
	00:22:50,559 --> 00:22:57,640
	turn these into like actual questions um

	535
	00:22:54,240 --> 00:22:59,640
	and answers about how you could write uh

	536
	00:22:57,640 --> 00:23:01,799
	solve programming

	537
	00:22:59,640 --> 00:23:04,080
	problems and

	538
	00:23:01,799 --> 00:23:05,600
	um because this is scraped from stack

	539
	00:23:04,080 --> 00:23:09,159
	Overflow there's no restriction that

	540
	00:23:05,600 --> 00:23:10,520
	this is from the python standard Library

	541
	00:23:09,159 --> 00:23:13,200
	which also means that it can cover a

	542
	00:23:10,520 --> 00:23:14,919
	very wide variety of libraries and it's

	543
	00:23:13,200 --> 00:23:16,760
	approximately according to the

	544
	00:23:14,919 --> 00:23:20,320
	popularity of the libraries because we

	545
	00:23:16,760 --> 00:23:24,159
	took popular posts so um that's a a good

	546
	00:23:20,320 --> 00:23:25,400
	thing uh you know it it is a reasonable

	547
	00:23:24,159 --> 00:23:26,559
	way to come up with a realistic

	548
	00:23:25,400 --> 00:23:29,520
	distribution of libraries that you

	549
	00:23:26,559 --> 00:23:31,799
	should be looking at um odex adds

	550
	00:23:29,520 --> 00:23:34,159
	execution based evaluation previously

	551
	00:23:31,799 --> 00:23:36,679
	what we had was we only had the snippet

	552
	00:23:34,159 --> 00:23:40,600
	that was able to solve the problem as

	553
	00:23:36,679 --> 00:23:42,360
	opposed to um as opposed to being able

	554
	00:23:40,600 --> 00:23:46,880
	to execute unit

	555
	00:23:42,360 --> 00:23:49,440
	tests and just to show how this has a

	556
	00:23:46,880 --> 00:23:52,000
	broader variety of libraries on the top

	557
	00:23:49,440 --> 00:23:53,919
	we have the distribution of odex

	558
	00:23:52,000 --> 00:23:57,320
	libraries and we can see about half of

	559
	00:23:53,919 --> 00:23:59,600
	them use libraries and this includes a

	560
	00:23:57,320 --> 00:24:01,279
	variety of things including pandas

	561
	00:23:59,600 --> 00:24:04,799
	numpy

	562
	00:24:01,279 --> 00:24:06,400
	um reg o selections you know all of

	563
	00:24:04,799 --> 00:24:09,279
	these should be libraries that look

	564
	00:24:06,400 --> 00:24:14,559
	familiar to you um in contrast if we

	565
	00:24:09,279 --> 00:24:17,200
	look at human eval human eval is right

	566
	00:24:14,559 --> 00:24:18,840
	here so you can see almost all of the

	567
	00:24:17,200 --> 00:24:20,600
	questions require no libraries and all

	568
	00:24:18,840 --> 00:24:22,120
	of the other ones require libraries that

	569
	00:24:20,600 --> 00:24:24,360
	were included in the pipe onstead

	570
	00:24:22,120 --> 00:24:27,640
	libraries so

	571
	00:24:24,360 --> 00:24:29,120
	um in reality this is probably more what

	572
	00:24:27,640 --> 00:24:30,120
	your program in queries are going to

	573
	00:24:29,120 --> 00:24:31,240
	look like they're not going to look like

	574
	00:24:30,120 --> 00:24:33,600
	lead code they're going to look like

	575
	00:24:31,240 --> 00:24:33,600
	using

	576
	00:24:35,360 --> 00:24:42,080
	APS so um originally when we did conal

	577
	00:24:40,039 --> 00:24:44,200
	we didn't use execution based evaluation

	578
	00:24:42,080 --> 00:24:47,480
	because creating unit tests uh for lots

	579
	00:24:44,200 --> 00:24:51,360
	of stack Overflow posts is hard

	580
	00:24:47,480 --> 00:24:53,640
	um specifically there's two issues the

	581
	00:24:51,360 --> 00:24:55,000
	first one is that it requires that code

	582
	00:24:53,640 --> 00:24:58,880
	be easily

	583
	00:24:55,000 --> 00:25:02,320
	executable um now think about

	584
	00:24:58,880 --> 00:25:04,559
	how you would do that for Matt plot lib

	585
	00:25:02,320 --> 00:25:06,200
	for example how would you create a unit

	586
	00:25:04,559 --> 00:25:08,080
	test to test whether Matt plot lib

	587
	00:25:06,200 --> 00:25:10,760
	successfully created a bar chart for

	588
	00:25:08,080 --> 00:25:12,440
	something it's kind of tough right you

	589
	00:25:10,760 --> 00:25:13,840
	like you would have to get the image and

	590
	00:25:12,440 --> 00:25:16,919
	you'd have to confirm that the image was

	591
	00:25:13,840 --> 00:25:21,200
	a bar chart and uh other things like

	592
	00:25:16,919 --> 00:25:22,720
	that um even worse what if it was uh

	593
	00:25:21,200 --> 00:25:25,600
	kind of like a server framework like

	594
	00:25:22,720 --> 00:25:27,440
	ajango how would you confirm that ajango

	595
	00:25:25,600 --> 00:25:30,559
	you know server is working appropriately

	596
	00:25:27,440 --> 00:25:32,600
	and that's kind of tricky so um actually

	597
	00:25:30,559 --> 00:25:34,480
	coming up with realistic unit tests for

	598
	00:25:32,600 --> 00:25:36,919
	real programs can be

	599
	00:25:34,480 --> 00:25:38,840
	difficult um another problem with

	600
	00:25:36,919 --> 00:25:41,640
	execution based evaluation is it ignores

	601
	00:25:38,840 --> 00:25:45,320
	stylistic considerations so I could

	602
	00:25:41,640 --> 00:25:48,279
	write very spaghetti like very spaghetti

	603
	00:25:45,320 --> 00:25:50,200
	code and as long as it executed properly

	604
	00:25:48,279 --> 00:25:52,559
	it would still be judged as correct and

	605
	00:25:50,200 --> 00:25:54,399
	sometimes that's actually an issue so

	606
	00:25:52,559 --> 00:25:56,360
	usually it's not a problem because

	607
	00:25:54,399 --> 00:25:58,600
	language models write reasonably good

	608
	00:25:56,360 --> 00:26:00,600
	code but sometimes you want to match the

	609
	00:25:58,600 --> 00:26:05,039
	or other things like that

	610
	00:26:00,600 --> 00:26:06,559
	so some alternatives are blue score

	611
	00:26:05,039 --> 00:26:09,000
	which we've talked about before it's

	612
	00:26:06,559 --> 00:26:12,679
	basically count calculating the engram

	613
	00:26:09,000 --> 00:26:16,919
	overlap between a gold standard human uh

	614
	00:26:12,679 --> 00:26:20,440
	implementation and a uh in the system

	615
	00:26:16,919 --> 00:26:24,000
	output and there's also specifically

	616
	00:26:20,440 --> 00:26:26,480
	adapted methods for evaluating code and

	617
	00:26:24,000 --> 00:26:29,080
	so there's a method called code blue and

	618
	00:26:26,480 --> 00:26:31,360
	basically the way code blue works is it

	619
	00:26:29,080 --> 00:26:35,240
	also considers the syntax and semantic

	620
	00:26:31,360 --> 00:26:37,080
	flow of the code so it measures overlap

	621
	00:26:35,240 --> 00:26:40,120
	between

	622
	00:26:37,080 --> 00:26:42,120
	strings in the original code but it also

	623
	00:26:40,120 --> 00:26:48,640
	considers overlap between the syntax

	624
	00:26:42,120 --> 00:26:53,000
	trees of the code and uh whether the

	625
	00:26:48,640 --> 00:26:56,320
	um these like semantic information flow

	626
	00:26:53,000 --> 00:26:57,919
	graphs look similar so uh all all of

	627
	00:26:56,320 --> 00:26:59,440
	these things work together to calculate

	628
	00:26:57,919 --> 00:27:02,720
	the C

	629
	00:26:59,440 --> 00:27:04,480
	St one thing I I should mention is how

	630
	00:27:02,720 --> 00:27:06,840
	do we get these syntax trees in the

	631
	00:27:04,480 --> 00:27:09,039
	first place um for example if we're

	632
	00:27:06,840 --> 00:27:12,919
	talking about python there's a python

	633
	00:27:09,039 --> 00:27:14,760
	Library uh for ab abstract syntax tree

	634
	00:27:12,919 --> 00:27:16,559
	it's just part of the standard library

	635
	00:27:14,760 --> 00:27:18,320
	and it's necessary to run the python

	636
	00:27:16,559 --> 00:27:20,559
	interpreter so you can just get these

	637
	00:27:18,320 --> 00:27:24,320
	trees directly from the python ASD

	638
	00:27:20,559 --> 00:27:25,880
	Library uh not hard to do uh for this I

	639
	00:27:24,320 --> 00:27:27,840
	forget what they did in the code blue

	640
	00:27:25,880 --> 00:27:30,679
	thing but there are uh analyzers that

	641
	00:27:27,840 --> 00:27:32,120
	allow you to analyze this control FL so

	642
	00:27:30,679 --> 00:27:34,159
	this is taking advantage of the fact

	643
	00:27:32,120 --> 00:27:37,440
	that code is you know predictable it has

	644
	00:27:34,159 --> 00:27:41,480
	predictable syntax and you can you

	645
	00:27:37,440 --> 00:27:43,960
	can6 um one disadvantage of blue and

	646
	00:27:41,480 --> 00:27:45,799
	code blue of course is that you know you

	647
	00:27:43,960 --> 00:27:47,679
	can write two very different looking

	648
	00:27:45,799 --> 00:27:49,559
	programs that actually are both correct

	649
	00:27:47,679 --> 00:27:51,799
	and blue will underestimate the goodness

	650
	00:27:49,559 --> 00:27:54,440
	of those programs so maybe using both of

	651
	00:27:51,799 --> 00:27:57,159
	them together is uh is

	652
	00:27:54,440 --> 00:28:00,120
	appropriate uh if if you can write unit

	653
	00:27:57,159 --> 00:28:00,120
	Test please

	654
	00:28:00,559 --> 00:28:04,279
	um another one which I'll just cover

	655
	00:28:02,600 --> 00:28:05,399
	very briefly we talked about BT score

	656
	00:28:04,279 --> 00:28:08,159
	before when I was talking about

	657
	00:28:05,399 --> 00:28:11,120
	evaluation of uh you know generated text

	658
	00:28:08,159 --> 00:28:13,480
	and there's also code BT score which um

	659
	00:28:11,120 --> 00:28:15,799
	we uh we created here at

	660
	00:28:13,480 --> 00:28:20,080
	CMU and it's basically an embedding

	661
	00:28:15,799 --> 00:28:21,760
	based metric uh to compare code and so

	662
	00:28:20,080 --> 00:28:23,399
	Bert score if you remember basically

	663
	00:28:21,760 --> 00:28:25,679
	what it did is it calculated the coign

	664
	00:28:23,399 --> 00:28:27,840
	similarity between each of the tokens uh

	665
	00:28:25,679 --> 00:28:30,159
	between a generated text and a reference

	666
	00:28:27,840 --> 00:28:34,279
	text we do exactly the same thing for

	667
	00:28:30,159 --> 00:28:36,080
	code um so we calculate the Sim cosine

	668
	00:28:34,279 --> 00:28:39,200
	similarity between tokens for a

	669
	00:28:36,080 --> 00:28:42,960
	reference code and generated

	670
	00:28:39,200 --> 00:28:45,000
	code and we released a model called

	671
	00:28:42,960 --> 00:28:46,559
	codir which was basically Bert but

	672
	00:28:45,000 --> 00:28:49,440
	continued trained on lots and lots of

	673
	00:28:46,559 --> 00:28:51,840
	code uh that allowed us to do that and

	674
	00:28:49,440 --> 00:28:55,480
	um basically we were able to demonstrate

	675
	00:28:51,840 --> 00:28:59,200
	that this gave better correlation both

	676
	00:28:55,480 --> 00:29:01,480
	with final execution accuracy and with

	677
	00:28:59,200 --> 00:29:05,200
	human judgments of whether the the code

	678
	00:29:01,480 --> 00:29:08,000
	was correct and so um some people uh

	679
	00:29:05,200 --> 00:29:09,559
	created a data set of human correctness

	680
	00:29:08,000 --> 00:29:12,559
	judgments and we were able to put a

	681
	00:29:09,559 --> 00:29:14,240
	little better with that as well um why

	682
	00:29:12,559 --> 00:29:15,640
	do we care about correlation with

	683
	00:29:14,240 --> 00:29:17,399
	execution

	684
	00:29:15,640 --> 00:29:20,200
	accuracy

	685
	00:29:17,399 --> 00:29:22,320
	um this is important in the cases when

	686
	00:29:20,200 --> 00:29:23,559
	we can't create unit tests or when

	687
	00:29:22,320 --> 00:29:26,120
	creating unit test would be too

	688
	00:29:23,559 --> 00:29:27,519
	expensive so this gives us a better

	689
	00:29:26,120 --> 00:29:30,640
	approximation for what we would get if

	690
	00:29:27,519 --> 00:29:30,640
	we ran tests

	691
	00:29:39,840 --> 00:29:45,000
	in yeah so we did not we did not

	692
	00:29:42,600 --> 00:29:46,799
	consider code structure here uh would

	693
	00:29:45,000 --> 00:29:48,480
	different variable names affect it yes

	694
	00:29:46,799 --> 00:29:50,159
	different variable names would affect it

	695
	00:29:48,480 --> 00:29:51,799
	but not as much as the other metrics

	696
	00:29:50,159 --> 00:29:53,960
	which is why it's better why it has

	697
	00:29:51,799 --> 00:29:56,720
	better

	698
	00:29:53,960 --> 00:30:00,000
	correlations and like for example

	699
	00:29:56,720 --> 00:30:03,679
	codir I imagine probably gives very

	700
	00:30:00,000 --> 00:30:05,120
	similar representations to I and J just

	701
	00:30:03,679 --> 00:30:07,960
	because they're both used in iterators

	702
	00:30:05,120 --> 00:30:09,039
	all the time whereas uh a normal Burt

	703
	00:30:07,960 --> 00:30:10,960
	model would give very different

	704
	00:30:09,039 --> 00:30:12,760
	representations to I and J right because

	705
	00:30:10,960 --> 00:30:14,960
	I is like a personal pronoun and J is

	706
	00:30:12,760 --> 00:30:17,200
	not so um that's the reason why

	707
	00:30:14,960 --> 00:30:20,399
	continued training would

	708
	00:30:17,200 --> 00:30:24,799
	help cool any other

	709
	00:30:20,399 --> 00:30:26,640
	things okay so another um another place

	710
	00:30:24,799 --> 00:30:29,480
	where code generation can be useful uh

	711
	00:30:26,640 --> 00:30:33,440
	we had the example of collab uh is in

	712
	00:30:29,480 --> 00:30:36,200
	collab notebooks and this or in uh data

	713
	00:30:33,440 --> 00:30:38,519
	science notebooks this paper was by uh

	714
	00:30:36,200 --> 00:30:41,440
	Google so this might actually even be

	715
	00:30:38,519 --> 00:30:43,960
	used in the collab thing because collab

	716
	00:30:41,440 --> 00:30:45,640
	is a Google thing um but data data

	717
	00:30:43,960 --> 00:30:47,320
	science notebooks allow for incremental

	718
	00:30:45,640 --> 00:30:50,519
	implementation I'm sure a lot of people

	719
	00:30:47,320 --> 00:30:53,559
	here or almost everybody here uses them

	720
	00:30:50,519 --> 00:30:55,279
	um and another interesting thing is say

	721
	00:30:53,559 --> 00:30:57,519
	allow for evaluation of code generation

	722
	00:30:55,279 --> 00:30:58,960
	in context uh or incremental code

	723
	00:30:57,519 --> 00:31:00,639
	generation

	724
	00:30:58,960 --> 00:31:02,720
	and so you start out with like a

	725
	00:31:00,639 --> 00:31:04,880
	notebook and then you have AAL

	726
	00:31:02,720 --> 00:31:06,600
	languageand and then youate the output

	727
	00:31:04,880 --> 00:31:09,240
	AAL language command you generate the

	728
	00:31:06,600 --> 00:31:10,799
	output etc etc so this is an extal

	729
	00:31:09,240 --> 00:31:14,519
	example from the STA

	730
	00:31:10,799 --> 00:31:17,519
	set um so this paper is very nice it it

	731
	00:31:14,519 --> 00:31:20,320
	has a lot of uh you know it's a nice

	732
	00:31:17,519 --> 00:31:21,720
	data set one other thing that was really

	733
	00:31:20,320 --> 00:31:24,200
	interesting from this paper is it

	734
	00:31:21,720 --> 00:31:27,919
	demonstrated the problem of data leakage

	735
	00:31:24,200 --> 00:31:29,679
	in evaluating models and this is a Rel

	736
	00:31:27,919 --> 00:31:32,440
	relatively large problem I don't know if

	737
	00:31:29,679 --> 00:31:33,799
	we have a silver bullet solution for

	738
	00:31:32,440 --> 00:31:36,120
	this but it's an important thing to be

	739
	00:31:33,799 --> 00:31:38,120
	aware of uh not just for code generation

	740
	00:31:36,120 --> 00:31:39,639
	but these are examples from code

	741
	00:31:38,120 --> 00:31:43,519
	generation

	742
	00:31:39,639 --> 00:31:45,679
	so here um in the arcade data set they

	743
	00:31:43,519 --> 00:31:48,519
	basically both evaluated existing

	744
	00:31:45,679 --> 00:31:51,720
	notebooks and they evaluated notebooks

	745
	00:31:48,519 --> 00:31:53,279
	that um existing notebooks that they got

	746
	00:31:51,720 --> 00:31:55,960
	from the web and they evaluated

	747
	00:31:53,279 --> 00:31:59,000
	notebooks that they actually created

	748
	00:31:55,960 --> 00:32:00,399
	themselves and there's very very Stark

	749
	00:31:59,000 --> 00:32:02,600
	difference between the notebooks that

	750
	00:32:00,399 --> 00:32:04,440
	were created on the web and the

	751
	00:32:02,600 --> 00:32:07,399
	notebooks that they evaluated themselves

	752
	00:32:04,440 --> 00:32:10,159
	so like most of the code generation

	753
	00:32:07,399 --> 00:32:11,679
	models except for Palm uh which was the

	754
	00:32:10,159 --> 00:32:14,760
	best model when they created this data

	755
	00:32:11,679 --> 00:32:17,360
	set did really poorly or did really well

	756
	00:32:14,760 --> 00:32:21,120
	on the existing data and quite poorly on

	757
	00:32:17,360 --> 00:32:25,279
	the new data um which is probably an

	758
	00:32:21,120 --> 00:32:28,159
	indication of um probably an indication

	759
	00:32:25,279 --> 00:32:29,720
	of the fact that you know this is to

	760
	00:32:28,159 --> 00:32:32,240
	some extent leaked into the training

	761
	00:32:29,720 --> 00:32:35,320
	data of the language models there was

	762
	00:32:32,240 --> 00:32:37,760
	also a very recent

	763
	00:32:35,320 --> 00:32:40,240
	um paper actually I think this might be

	764
	00:32:37,760 --> 00:32:43,159
	2024 there was a very recent paper that

	765
	00:32:40,240 --> 00:32:45,880
	did a similar thing uh where they

	766
	00:32:43,159 --> 00:32:48,440
	evaluated on human ofel and then their

	767
	00:32:45,880 --> 00:32:52,000
	live codebench in live codebench

	768
	00:32:48,440 --> 00:32:55,639
	basically what they did is they tried to

	769
	00:32:52,000 --> 00:32:58,519
	pick problems from Le code and other

	770
	00:32:55,639 --> 00:33:00,519
	websites that were more recent versus

	771
	00:32:58,519 --> 00:33:01,960
	less recent and they have some really

	772
	00:33:00,519 --> 00:33:04,880
	nice graphs in their paper where they

	773
	00:33:01,960 --> 00:33:06,519
	demonstrate that the less recent ones

	774
	00:33:04,880 --> 00:33:08,159
	before the training cut off have like a

	775
	00:33:06,519 --> 00:33:10,080
	high accuracy and then suddenly it drops

	776
	00:33:08,159 --> 00:33:12,639
	right at the trading C off of the the

	777
	00:33:10,080 --> 00:33:13,480
	models so this is something to to be

	778
	00:33:12,639 --> 00:33:17,360
	aware

	779
	00:33:13,480 --> 00:33:20,519
	of and what this figure is showing here

	780
	00:33:17,360 --> 00:33:24,039
	is this figure is showing on the xaxis

	781
	00:33:20,519 --> 00:33:26,840
	pass it one on the Live code bench easy

	782
	00:33:24,039 --> 00:33:28,679
	and then pass it one on human ofel so we

	783
	00:33:26,840 --> 00:33:31,480
	see this kn

	784
	00:33:28,679 --> 00:33:34,039
	correlation between

	785
	00:33:31,480 --> 00:33:35,919
	essentially like passing on life code

	786
	00:33:34,039 --> 00:33:37,399
	bench easy and passing on human ofel

	787
	00:33:35,919 --> 00:33:40,000
	then we have this group of models that

	788
	00:33:37,399 --> 00:33:42,159
	are kind of like up here and these are

	789
	00:33:40,000 --> 00:33:43,960
	ones where basically it's likely that

	790
	00:33:42,159 --> 00:33:46,480
	human ofel leaked into the training data

	791
	00:33:43,960 --> 00:33:48,840
	because they're getting better scores on

	792
	00:33:46,480 --> 00:33:50,919
	human ofel than you would expect that

	793
	00:33:48,840 --> 00:33:53,360
	they get uh you know just looking at

	794
	00:33:50,919 --> 00:33:55,360
	their uh you know performance on another

	795
	00:33:53,360 --> 00:33:57,320
	data set there's also a nice like

	796
	00:33:55,360 --> 00:34:00,000
	analogous one for math reasoning

	797
	00:33:57,320 --> 00:34:01,519
	problems um like this so this is

	798
	00:34:00,000 --> 00:34:03,039
	definitely something to be aware of if

	799
	00:34:01,519 --> 00:34:04,559
	you're looking only at like very

	800
	00:34:03,039 --> 00:34:06,200
	standard benchmarks that people are

	801
	00:34:04,559 --> 00:34:11,159
	trading

	802
	00:34:06,200 --> 00:34:11,159
	in cool um any questions about

	803
	00:34:12,119 --> 00:34:19,240
	this okay um another data set uh that I

	804
	00:34:17,720 --> 00:34:20,599
	I really like the concept of and

	805
	00:34:19,240 --> 00:34:22,919
	recently it's gotten a little bit of

	806
	00:34:20,599 --> 00:34:25,399
	Buzz because it was used in a um an

	807
	00:34:22,919 --> 00:34:28,399
	evaluation of a new coding assistant

	808
	00:34:25,399 --> 00:34:30,480
	called Devon but this is um

	809
	00:34:28,399 --> 00:34:32,240
	something called sbench and it's issues

	810
	00:34:30,480 --> 00:34:34,639
	from GitHub and code

	811
	00:34:32,240 --> 00:34:37,119
	bases uh is the input and you want to

	812
	00:34:34,639 --> 00:34:39,480
	generate a poll request to basically uh

	813
	00:34:37,119 --> 00:34:42,919
	solve these issues and so your input is

	814
	00:34:39,480 --> 00:34:45,800
	like data leak in gbdt due to warm start

	815
	00:34:42,919 --> 00:34:48,800
	this is about non standard then you have

	816
	00:34:45,800 --> 00:34:51,159
	the code base um it generates a PR for

	817
	00:34:48,800 --> 00:34:53,079
	you and then it's run through the unit

	818
	00:34:51,159 --> 00:34:55,919
	tests to see if it passes all the unit

	819
	00:34:53,079 --> 00:34:57,160
	test post PRS so it's very similar to

	820
	00:34:55,919 --> 00:34:59,240
	you know what you would be doing in a

	821
	00:34:57,160 --> 00:35:01,280
	well Main software project you open a

	822
	00:34:59,240 --> 00:35:05,240
	issue and then you open a poll request

	823
	00:35:01,280 --> 00:35:07,800
	to fix an issue um this requires things

	824
	00:35:05,240 --> 00:35:10,240
	like long context understanding um being

	825
	00:35:07,800 --> 00:35:13,200
	able to do very precise implementations

	826
	00:35:10,240 --> 00:35:14,720
	based on large software projects and

	827
	00:35:13,200 --> 00:35:17,920
	right now the state-of-the-art on this

	828
	00:35:14,720 --> 00:35:20,680
	is at about 14% so it's definitely not a

	829
	00:35:17,920 --> 00:35:23,119
	solv problem at all um in the original

	830
	00:35:20,680 --> 00:35:27,920
	paper uh the the state-of-the-art method

	831
	00:35:23,119 --> 00:35:29,400
	was like 6% or something like that so um

	832
	00:35:27,920 --> 00:35:32,079
	I imagine that we're not going to get up

	833
	00:35:29,400 --> 00:35:33,880
	to 90% anytime soon because it's

	834
	00:35:32,079 --> 00:35:35,720
	probably solving the easier ones and the

	835
	00:35:33,880 --> 00:35:37,280
	harder ones are you know far beyond the

	836
	00:35:35,720 --> 00:35:39,920
	ability of any language model we have at

	837
	00:35:37,280 --> 00:35:42,320
	the moment um but I I really like this

	838
	00:35:39,920 --> 00:35:43,960
	Benchmark one caveat if you really like

	839
	00:35:42,320 --> 00:35:45,520
	this Benchmark is that it's kind of

	840
	00:35:43,960 --> 00:35:47,760
	heavy to run so you need to be a little

	841
	00:35:45,520 --> 00:35:51,000
	bit careful uh because you need to pull

	842
	00:35:47,760 --> 00:35:54,280
	in like full repositories to um to run

	843
	00:35:51,000 --> 00:35:56,319
	on so yeah be a little

	844
	00:35:54,280 --> 00:35:57,920
	bit sorry there's so many like

	845
	00:35:56,319 --> 00:35:59,640
	interesting data sets recently in this

	846
	00:35:57,920 --> 00:36:01,079
	area that I I spent a lot of time on

	847
	00:35:59,640 --> 00:36:04,240
	data set so I'll try to go a little bit

	848
	00:36:01,079 --> 00:36:06,200
	more quickly but um uh a final one is

	849
	00:36:04,240 --> 00:36:09,359
	design to code and this is also a very

	850
	00:36:06,200 --> 00:36:11,520
	recent data set um basically the idea is

	851
	00:36:09,359 --> 00:36:16,359
	code generation from websites so your

	852
	00:36:11,520 --> 00:36:18,119
	input is a website and your output is uh

	853
	00:36:16,359 --> 00:36:22,520
	like JavaScript code that implements

	854
	00:36:18,119 --> 00:36:24,960
	that website and or or css or HTML code

	855
	00:36:22,520 --> 00:36:26,880
	that implements the website so I I

	856
	00:36:24,960 --> 00:36:30,119
	really like this because you know it's a

	857
	00:36:26,880 --> 00:36:32,280
	good test bed for multi modal models and

	858
	00:36:30,119 --> 00:36:34,040
	there aren't a whole lot of strong open

	859
	00:36:32,280 --> 00:36:36,160
	source multimodal models that can solve

	860
	00:36:34,040 --> 00:36:36,960
	this at the moment so I think it's kind

	861
	00:36:36,160 --> 00:36:39,720
	of

	862
	00:36:36,960 --> 00:36:41,480
	cool um they also proposed a design to

	863
	00:36:39,720 --> 00:36:43,480
	code model that does the best on this

	864
	00:36:41,480 --> 00:36:47,119
	data set out of uh you know any of the

	865
	00:36:43,480 --> 00:36:47,119
	open source models but it's still far

	866
	00:36:47,400 --> 00:36:53,040
	from and then the question becomes how

	867
	00:36:50,680 --> 00:36:56,079
	do they um evaluate this in the first

	868
	00:36:53,040 --> 00:36:59,440
	place and basically the idea is that

	869
	00:36:56,079 --> 00:37:01,400
	they do highle visual similarity and so

	870
	00:36:59,440 --> 00:37:03,920
	they calculate visual embeddings of the

	871
	00:37:01,400 --> 00:37:06,119
	generated sites and then they also do

	872
	00:37:03,920 --> 00:37:08,240
	lowl element similarity so they try to

	873
	00:37:06,119 --> 00:37:10,440
	identify all of the elements in the

	874
	00:37:08,240 --> 00:37:12,119
	generated web page and make sure that uh

	875
	00:37:10,440 --> 00:37:15,720
	they recall all of the generated

	876
	00:37:12,119 --> 00:37:18,760
	elements so um I think this is nice one

	877
	00:37:15,720 --> 00:37:21,000
	thing if you notice um if you use even

	878
	00:37:18,760 --> 00:37:25,960
	state-ofthe-art like closed models like

	879
	00:37:21,000 --> 00:37:28,040
	CLA 3 or um GPD 4 is they're really bad

	880
	00:37:25,960 --> 00:37:29,440
	at this recall they it can generate

	881
	00:37:28,040 --> 00:37:31,800
	something that looks like maybe a little

	882
	00:37:29,440 --> 00:37:33,839
	bit similar but it will be missing like

	883
	00:37:31,800 --> 00:37:35,720
	the elements the design will be off you

	884
	00:37:33,839 --> 00:37:37,720
	know other stuff like that so I think

	885
	00:37:35,720 --> 00:37:41,079
	even in the closed like strong models

	886
	00:37:37,720 --> 00:37:41,079
	this is not a Sol

	887
	00:37:41,319 --> 00:37:47,079
	problem cool uh

	888
	00:37:45,000 --> 00:37:49,880
	yeah

	889
	00:37:47,079 --> 00:37:51,880
	problem um so why is that a hard problem

	890
	00:37:49,880 --> 00:37:54,200
	for the models I don't actually have a

	891
	00:37:51,880 --> 00:37:57,200
	really confident answer to that but I

	892
	00:37:54,200 --> 00:37:57,200
	think

	893
	00:38:00,240 --> 00:38:05,200
	so one thing I can tell you is that they

	894
	00:38:02,839 --> 00:38:08,839
	are able to

	895
	00:38:05,200 --> 00:38:12,000
	improve um so they're able to generate

	896
	00:38:08,839 --> 00:38:14,720
	something and then I say no that's bad

	897
	00:38:12,000 --> 00:38:16,160
	please like make it better and it's

	898
	00:38:14,720 --> 00:38:17,800
	generally better the second time

	899
	00:38:16,160 --> 00:38:19,920
	especially if you give specific things

	900
	00:38:17,800 --> 00:38:22,319
	like oh uh but the background on the

	901
	00:38:19,920 --> 00:38:25,160
	generated site is white but actually it

	902
	00:38:22,319 --> 00:38:27,599
	should be black and if you think about

	903
	00:38:25,160 --> 00:38:31,480
	like even a skilled human programmer do

	904
	00:38:27,599 --> 00:38:35,119
	you think you could write like website

	905
	00:38:31,480 --> 00:38:37,680
	code and then view it once and then it

	906
	00:38:35,119 --> 00:38:40,319
	would be correct I think you probably

	907
	00:38:37,680 --> 00:38:42,160
	couldn't right and so like we're asking

	908
	00:38:40,319 --> 00:38:44,040
	models to do essentially the same thing

	909
	00:38:42,160 --> 00:38:46,920
	except they're like even worse than us

	910
	00:38:44,040 --> 00:38:48,560
	and you know keeping track of all the V

	911
	00:38:46,920 --> 00:38:50,720
	visual elements and stuff so I think

	912
	00:38:48,560 --> 00:38:52,480
	it's more like this problem probably

	913
	00:38:50,720 --> 00:38:54,720
	just needs iterative refinement

	914
	00:38:52,480 --> 00:38:58,839
	otherwise it's like asking too much of a

	915
	00:38:54,720 --> 00:39:02,640
	model maybe I don't know

	916
	00:38:58,839 --> 00:39:04,520
	cool okay so um let's go into methods

	917
	00:39:02,640 --> 00:39:06,920
	and code generation has some unique

	918
	00:39:04,520 --> 00:39:09,400
	things um the basic method that you can

	919
	00:39:06,920 --> 00:39:11,240
	always use is a code generating LM and

	920
	00:39:09,400 --> 00:39:13,040
	so you feed in previous code or you feed

	921
	00:39:11,240 --> 00:39:16,040
	in whatever context you have into the LM

	922
	00:39:13,040 --> 00:39:18,079
	and you generate um uh from it and

	923
	00:39:16,040 --> 00:39:20,079
	virtually all Serius LMS are trained on

	924
	00:39:18,079 --> 00:39:23,079
	code nowadays like I I just mentioned

	925
	00:39:20,079 --> 00:39:23,079
	before

	926
	00:39:23,119 --> 00:39:29,920
	um one one important thing here is uh

	927
	00:39:28,560 --> 00:39:31,240
	when you're generating if you're

	928
	00:39:29,920 --> 00:39:33,040
	generating for something like code

	929
	00:39:31,240 --> 00:39:34,480
	generation I definitely suggest that you

	930
	00:39:33,040 --> 00:39:36,119
	modify your temperature settings

	931
	00:39:34,480 --> 00:39:38,359
	appropriately and set it to a low

	932
	00:39:36,119 --> 00:39:42,160
	temperature um otherwise you'll get kind

	933
	00:39:38,359 --> 00:39:45,079
	of crazy uh code but if you set it to a

	934
	00:39:42,160 --> 00:39:45,079
	low temperature you can get

	935
	00:39:46,440 --> 00:39:52,160
	better anyway um one really core

	936
	00:39:49,640 --> 00:39:54,240
	capability of code LMS especially ones

	937
	00:39:52,160 --> 00:39:55,599
	that you use in your IDE like uh

	938
	00:39:54,240 --> 00:39:58,160
	co-pilot is

	939
	00:39:55,599 --> 00:40:00,000
	infilling and um

	940
	00:39:58,160 --> 00:40:03,680
	the the paper that proposed this is

	941
	00:40:00,000 --> 00:40:05,920
	actually by Daniel Freed at LTI here and

	942
	00:40:03,680 --> 00:40:09,160
	um

	943
	00:40:05,920 --> 00:40:11,240
	the basically what you want to do often

	944
	00:40:09,160 --> 00:40:13,000
	is you have previous code you have next

	945
	00:40:11,240 --> 00:40:14,680
	code and you want to just fill in like a

	946
	00:40:13,000 --> 00:40:17,960
	line that's missing like you want to add

	947
	00:40:14,680 --> 00:40:19,040
	an extra you know if statement or or

	948
	00:40:17,960 --> 00:40:22,720
	some sort of

	949
	00:40:19,040 --> 00:40:24,880
	modification and so the way that at

	950
	00:40:22,720 --> 00:40:27,000
	least this paper proposed it and the way

	951
	00:40:24,880 --> 00:40:29,800
	that I think most LMS are actually doing

	952
	00:40:27,000 --> 00:40:30,640
	this is they take a standard left to

	953
	00:40:29,800 --> 00:40:33,200
	right

	954
	00:40:30,640 --> 00:40:36,040
	LM and what they want to do is they want

	955
	00:40:33,200 --> 00:40:39,040
	to infill this code chunk and so what

	956
	00:40:36,040 --> 00:40:40,440
	they do is they put a mask in the place

	957
	00:40:39,040 --> 00:40:42,119
	where they want to fill the chunk which

	958
	00:40:40,440 --> 00:40:46,280
	would also be where your cursor is in

	959
	00:40:42,119 --> 00:40:49,960
	your IDE right uh at that point and then

	960
	00:40:46,280 --> 00:40:52,680
	they have Mas to zero and then at the

	961
	00:40:49,960 --> 00:40:57,400
	end they put mask to zero again and then

	962
	00:40:52,680 --> 00:40:59,000
	they output the like you know all of the

	963
	00:40:57,400 --> 00:41:01,040
	code that you want to generate there and

	964
	00:40:59,000 --> 00:41:02,839
	so you can just kind of arbitrarily

	965
	00:41:01,040 --> 00:41:05,480
	generate these trunks by pulling you

	966
	00:41:02,839 --> 00:41:07,000
	know masking out chunks uh putting in

	967
	00:41:05,480 --> 00:41:08,960
	The Mask token and then moving it to the

	968
	00:41:07,000 --> 00:41:10,440
	end of the sequence and then you can

	969
	00:41:08,960 --> 00:41:13,160
	just use a standard left to right Auto

	970
	00:41:10,440 --> 00:41:15,359
	regressive language model to solve this

	971
	00:41:13,160 --> 00:41:17,040
	problem so this is really important if

	972
	00:41:15,359 --> 00:41:18,520
	you want to build like a co-pilot style

	973
	00:41:17,040 --> 00:41:20,160
	thing and all of the code language

	974
	00:41:18,520 --> 00:41:23,680
	models that I talk about at the end of

	975
	00:41:20,160 --> 00:41:23,680
	this class uh use this

	976
	00:41:24,800 --> 00:41:30,440
	technique um another thing is there's

	977
	00:41:28,160 --> 00:41:33,760
	lots of available information uh for

	978
	00:41:30,440 --> 00:41:36,040
	learning coding things um or for solving

	979
	00:41:33,760 --> 00:41:38,880
	coding tasks this includes you know the

	980
	00:41:36,040 --> 00:41:40,440
	current code context of course um also

	981
	00:41:38,880 --> 00:41:41,920
	the description of the issue that you

	982
	00:41:40,440 --> 00:41:45,160
	want to be fixing like if you're solving

	983
	00:41:41,920 --> 00:41:49,240
	a poll request um repo context from

	984
	00:41:45,160 --> 00:41:51,880
	other files um what tabs you have open

	985
	00:41:49,240 --> 00:41:55,920
	uh so that that's also an important

	986
	00:41:51,880 --> 00:41:58,599
	thing and when GitHub co-pilot came out

	987
	00:41:55,920 --> 00:42:01,960
	they didn't really tell you the details

	988
	00:41:58,599 --> 00:42:04,480
	of how they were doing this but um

	989
	00:42:01,960 --> 00:42:09,079
	GitHub co-pilot is written in JavaScript

	990
	00:42:04,480 --> 00:42:11,839
	and uh there was a p PhD student I think

	991
	00:42:09,079 --> 00:42:14,000
	from maybe Georgia Tech or something uh

	992
	00:42:11,839 --> 00:42:16,839
	who or Master student who basically went

	993
	00:42:14,000 --> 00:42:19,160
	in and took the JavaScript and like Dem

	994
	00:42:16,839 --> 00:42:21,839
	minified it and like reverse engineered

	995
	00:42:19,160 --> 00:42:23,640
	what was actually happening um and uh

	996
	00:42:21,839 --> 00:42:26,680
	wrote A Blog about it and this blog is

	997
	00:42:23,640 --> 00:42:28,800
	is great uh so basically what uh

	998
	00:42:26,680 --> 00:42:32,200
	co-pilot was doing which also kind of

	999
	00:42:28,800 --> 00:42:33,839
	gives you a gold standard um way of uh

	1000
	00:42:32,200 --> 00:42:36,920
	looking

	1001
	00:42:33,839 --> 00:42:39,440
	at uh you know what kind of information

	1002
	00:42:36,920 --> 00:42:43,440
	is necessary to create a good model is

	1003
	00:42:39,440 --> 00:42:45,240
	first they extract um information for

	1004
	00:42:43,440 --> 00:42:47,400
	the prompt given the current document

	1005
	00:42:45,240 --> 00:42:49,240
	and the cursor position so they take the

	1006
	00:42:47,400 --> 00:42:51,720
	current document where is the cursor and

	1007
	00:42:49,240 --> 00:42:54,640
	what is before this and what is after

	1008
	00:42:51,720 --> 00:42:56,960
	this um they identify the relative path

	1009
	00:42:54,640 --> 00:42:59,960
	of the file and what language it's in so

	1010
	00:42:56,960 --> 00:43:01,760
	they they identifi python files or

	1011
	00:42:59,960 --> 00:43:04,240
	JavaScript files or

	1012
	00:43:01,760 --> 00:43:07,440
	whatever they find the most recently

	1013
	00:43:04,240 --> 00:43:09,800
	accessed 20 files in the same language

	1014
	00:43:07,440 --> 00:43:12,599
	so like if you've opened 20 tabs they

	1015
	00:43:09,800 --> 00:43:15,559
	keep track of which tab you had

	1016
	00:43:12,599 --> 00:43:18,280
	open um and then the actual prompt that

	1017
	00:43:15,559 --> 00:43:22,119
	they send over includes text that is

	1018
	00:43:18,280 --> 00:43:23,640
	before text that's after um similar

	1019
	00:43:22,119 --> 00:43:26,520
	files out of the 20 files that you've

	1020
	00:43:23,640 --> 00:43:29,480
	opened recently um also information from

	1021
	00:43:26,520 --> 00:43:31,760
	imported files and metadata about the

	1022
	00:43:29,480 --> 00:43:33,079
	language and the path so all of this is

	1023
	00:43:31,760 --> 00:43:37,079
	sent to the

	1024
	00:43:33,079 --> 00:43:38,720
	model um and so this is just basically

	1025
	00:43:37,079 --> 00:43:40,160
	it's really good prompt engineering

	1026
	00:43:38,720 --> 00:43:41,760
	right they're figuring out a good way to

	1027
	00:43:40,160 --> 00:43:44,200
	get all of the information that would be

	1028
	00:43:41,760 --> 00:43:45,680
	useful uh for getting this model to work

	1029
	00:43:44,200 --> 00:43:49,559
	into the

	1030
	00:43:45,680 --> 00:43:50,920
	prompt um so I there's much much more

	1031
	00:43:49,559 --> 00:43:52,839
	information in this plug it's a really

	1032
	00:43:50,920 --> 00:43:57,400
	nice blog if you uh if you want to see

	1033
	00:43:52,839 --> 00:43:57,400
	about it but um that's the basic

	1034
	00:43:57,640 --> 00:44:00,240
	any any

	1035
	00:44:01,240 --> 00:44:07,160
	questions okay

	1036
	00:44:03,520 --> 00:44:11,240
	cool yeah is this just what gets sent

	1037
	00:44:07,160 --> 00:44:13,520
	over to theot server or does

	1038
	00:44:11,240 --> 00:44:15,240
	copilot this is what gets sent over to

	1039
	00:44:13,520 --> 00:44:17,920
	the co-pilot server but the way they're

	1040
	00:44:15,240 --> 00:44:20,960
	sending it makes me guess that like all

	1041
	00:44:17,920 --> 00:44:22,839
	of this is red so like they also are

	1042
	00:44:20,960 --> 00:44:24,559
	considering I didn't mention it here but

	1043
	00:44:22,839 --> 00:44:26,000
	they're considering the token limit and

	1044
	00:44:24,559 --> 00:44:27,599
	other stuff like that so that kind of

	1045
	00:44:26,000 --> 00:44:30,760
	makes me feel like this is

	1046
	00:44:27,599 --> 00:44:30,760
	actually the

	1047
	00:44:32,240 --> 00:44:38,440
	pr uh cool

	1048
	00:44:35,359 --> 00:44:41,040
	so another uh thing that you can do is

	1049
	00:44:38,440 --> 00:44:42,520
	retrieval based code generation and

	1050
	00:44:41,040 --> 00:44:45,640
	retrieval based code

	1051
	00:44:42,520 --> 00:44:47,599
	generation uh basically what it does is

	1052
	00:44:45,640 --> 00:44:50,920
	it's like rag for code

	1053
	00:44:47,599 --> 00:44:53,240
	Generation Um and this has been around

	1054
	00:44:50,920 --> 00:44:55,640
	for a while including our work that I

	1055
	00:44:53,240 --> 00:44:57,680
	cited here and a few more in in

	1056
	00:44:55,640 --> 00:44:59,960
	2018 um

	1057
	00:44:57,680 --> 00:45:03,000
	and so one way you can do this is you

	1058
	00:44:59,960 --> 00:45:07,160
	can retrieve similar code from online

	1059
	00:45:03,000 --> 00:45:09,720
	and then use it to basically prompt a

	1060
	00:45:07,160 --> 00:45:11,920
	retrieval augmented language model uh

	1061
	00:45:09,720 --> 00:45:14,480
	this is good if you have a model that's

	1062
	00:45:11,920 --> 00:45:16,920
	not super good at code in the first

	1063
	00:45:14,480 --> 00:45:19,920
	place or you know it's making mistakes

	1064
	00:45:16,920 --> 00:45:21,680
	it's also good if you have a large code

	1065
	00:45:19,920 --> 00:45:23,040
	base like that's inter internal and you

	1066
	00:45:21,680 --> 00:45:24,200
	know the language model was not trained

	1067
	00:45:23,040 --> 00:45:26,359
	on it but you still want to use that

	1068
	00:45:24,200 --> 00:45:27,559
	code base for code generation so it's

	1069
	00:45:26,359 --> 00:45:29,599
	really good if you're working at like a

	1070
	00:45:27,559 --> 00:45:32,160
	big company for example that has a very

	1071
	00:45:29,599 --> 00:45:33,319
	constant coding style but hasn't trained

	1072
	00:45:32,160 --> 00:45:37,160
	its own

	1073
	00:45:33,319 --> 00:45:39,720
	LM um also particularly in code there's

	1074
	00:45:37,160 --> 00:45:43,559
	also documentation uh which can be

	1075
	00:45:39,720 --> 00:45:46,920
	retrieved and so we have new libraries

	1076
	00:45:43,559 --> 00:45:51,359
	all the time right and one frustrating

	1077
	00:45:46,920 --> 00:45:53,119
	thing when using like uh chat jpt or CLA

	1078
	00:45:51,359 --> 00:45:57,400
	or something like that when you're

	1079
	00:45:53,119 --> 00:45:59,559
	writing programs is that it can use old

	1080
	00:45:57,400 --> 00:46:03,480
	versions of libraries that are no longer

	1081
	00:45:59,559 --> 00:46:05,359
	compatible and so um in this paper uh

	1082
	00:46:03,480 --> 00:46:08,359
	which this is one of our papers too we

	1083
	00:46:05,359 --> 00:46:10,079
	called it DOC prompting um basically the

	1084
	00:46:08,359 --> 00:46:13,720
	idea is that

	1085
	00:46:10,079 --> 00:46:17,440
	you have your natural language input and

	1086
	00:46:13,720 --> 00:46:20,119
	then you look up uh similar thing

	1087
	00:46:17,440 --> 00:46:23,240
	similar documentation so you find like

	1088
	00:46:20,119 --> 00:46:25,319
	pigment is a general syntax highlighter

	1089
	00:46:23,240 --> 00:46:28,160
	uh so you can uh find syntax

	1090
	00:46:25,319 --> 00:46:31,160
	highlighting um you can also look up the

	1091
	00:46:28,160 --> 00:46:32,640
	lexer you can look up the HTML formatter

	1092
	00:46:31,160 --> 00:46:35,119
	and then all of the things that have

	1093
	00:46:32,640 --> 00:46:37,000
	similar documentation then you can uh

	1094
	00:46:35,119 --> 00:46:39,480
	append that to the prompt and then have

	1095
	00:46:37,000 --> 00:46:41,680
	that Genera output and we demonstrate

	1096
	00:46:39,480 --> 00:46:43,200
	that this is good both in general but

	1097
	00:46:41,680 --> 00:46:44,800
	also it's particularly good when you're

	1098
	00:46:43,200 --> 00:46:46,240
	dealing with new libraries that haven't

	1099
	00:46:44,800 --> 00:46:48,280
	been seen before or libraries that have

	1100
	00:46:46,240 --> 00:46:50,119
	been updated so this is another thing

	1101
	00:46:48,280 --> 00:46:53,000
	that you can

	1102
	00:46:50,119 --> 00:46:55,720
	do

	1103
	00:46:53,000 --> 00:46:57,520
	cool um another thing that you can do

	1104
	00:46:55,720 --> 00:47:00,040
	with code that you can't do easily with

	1105
	00:46:57,520 --> 00:47:04,040
	natural language is execution

	1106
	00:47:00,040 --> 00:47:06,119
	feedback and so this is a a paper where

	1107
	00:47:04,040 --> 00:47:09,359
	basically they do something that's

	1108
	00:47:06,119 --> 00:47:10,319
	rather simple but they generate multiple

	1109
	00:47:09,359 --> 00:47:13,359
	types of

	1110
	00:47:10,319 --> 00:47:14,559
	code or multiple instances of code so

	1111
	00:47:13,359 --> 00:47:16,880
	they basically sample different

	1112
	00:47:14,559 --> 00:47:19,960
	varieties of code and I was talking

	1113
	00:47:16,880 --> 00:47:22,720
	about like casset K right uh before

	1114
	00:47:19,960 --> 00:47:25,000
	casset K is good if you have some way to

	1115
	00:47:22,720 --> 00:47:26,520
	confirm which output is correct like you

	1116
	00:47:25,000 --> 00:47:28,040
	already have unit tests and you can run

	1117
	00:47:26,520 --> 00:47:29,440
	the unit test and identify which one

	1118
	00:47:28,040 --> 00:47:31,839
	passes the unit test or you can have a

	1119
	00:47:29,440 --> 00:47:34,160
	human check it but in the case when you

	1120
	00:47:31,839 --> 00:47:35,640
	can't do that what can you do and

	1121
	00:47:34,160 --> 00:47:38,079
	basically what you can do is you can

	1122
	00:47:35,640 --> 00:47:40,800
	execute all of the code Snippets that

	1123
	00:47:38,079 --> 00:47:43,839
	the model generated and check if the

	1124
	00:47:40,800 --> 00:47:48,520
	outputs overlap with each other and if

	1125
	00:47:43,839 --> 00:47:50,680
	you have um you know 30 programs that

	1126
	00:47:48,520 --> 00:47:53,680
	all generate very similar outputs then

	1127
	00:47:50,680 --> 00:47:55,079
	those outputs you know then that program

	1128
	00:47:53,680 --> 00:47:56,520
	is probably correct and then you can

	1129
	00:47:55,079 --> 00:48:00,000
	just pick one of them according to some

	1130
	00:47:56,520 --> 00:48:02,160
	criteria Ian specifically in this case

	1131
	00:48:00,000 --> 00:48:03,960
	they picked the program that has the

	1132
	00:48:02,160 --> 00:48:05,599
	lowest base risk like when we talked

	1133
	00:48:03,960 --> 00:48:09,040
	about minimum base risk and the decoding

	1134
	00:48:05,599 --> 00:48:10,839
	much so um they they basically execute a

	1135
	00:48:09,040 --> 00:48:12,800
	lot and then calculate the base risk of

	1136
	00:48:10,839 --> 00:48:17,000
	that

	1137
	00:48:12,800 --> 00:48:17,000
	that cool um

	1138
	00:48:17,680 --> 00:48:24,440
	yeah yeah and so like self consistency

	1139
	00:48:21,599 --> 00:48:26,079
	is a variety of Base risk um and they're

	1140
	00:48:24,440 --> 00:48:27,640
	using base risk here because outputs

	1141
	00:48:26,079 --> 00:48:30,720
	might not be exact the same but being

	1142
	00:48:27,640 --> 00:48:30,720
	closer is probably better

	1143
	00:48:34,160 --> 00:48:39,040
	than

	1144
	00:48:36,760 --> 00:48:40,559
	comp comparison of the code yeah that's

	1145
	00:48:39,040 --> 00:48:42,880
	a good question especially if you use

	1146
	00:48:40,559 --> 00:48:44,319
	something good like uh code BT score to

	1147
	00:48:42,880 --> 00:48:46,280
	do that comparison you might not even

	1148
	00:48:44,319 --> 00:48:50,280
	need to that's

	1149
	00:48:46,280 --> 00:48:50,280
	that I don't think they did that in

	1150
	00:48:50,559 --> 00:48:57,240
	this cool um another interesting thing

	1151
	00:48:54,920 --> 00:48:59,760
	um is there's

	1152
	00:48:57,240 --> 00:49:04,119
	several lines of work on fixing based on

	1153
	00:48:59,760 --> 00:49:06,720
	eror messages so the basic idea is you

	1154
	00:49:04,119 --> 00:49:08,160
	generate code you try to run it you get

	1155
	00:49:06,720 --> 00:49:13,280
	an airor message from it and then you

	1156
	00:49:08,160 --> 00:49:16,200
	feed that back to the llm um in order to

	1157
	00:49:13,280 --> 00:49:17,520
	you know correct the error and like llms

	1158
	00:49:16,200 --> 00:49:19,119
	if you give them an err and you give

	1159
	00:49:17,520 --> 00:49:20,839
	them buggy code they do have some

	1160
	00:49:19,119 --> 00:49:24,599
	capacity to do that especially as you

	1161
	00:49:20,839 --> 00:49:28,839
	get to theer llm so uh this is kind of a

	1162
	00:49:24,599 --> 00:49:31,200
	a nice uh paradigm this paper intercode

	1163
	00:49:28,839 --> 00:49:33,880
	actually generalizes this a bit and it's

	1164
	00:49:31,200 --> 00:49:38,359
	more recent that's why I cited it here

	1165
	00:49:33,880 --> 00:49:40,000
	and uh so this also um like says you can

	1166
	00:49:38,359 --> 00:49:42,640
	do single turn code generation you can

	1167
	00:49:40,000 --> 00:49:44,960
	also say oh could you please try again

	1168
	00:49:42,640 --> 00:49:46,400
	um you can also uh do planning and

	1169
	00:49:44,960 --> 00:49:48,160
	solving and other stuff like that so

	1170
	00:49:46,400 --> 00:49:49,960
	this is a good kind of like environment

	1171
	00:49:48,160 --> 00:49:52,079
	if you're interested in making these

	1172
	00:49:49,960 --> 00:49:56,720
	more like interactive coding assistance

	1173
	00:49:52,079 --> 00:49:56,720
	for example so you could take a look bre

	1174
	00:49:58,359 --> 00:50:03,359
	cool

	1175
	00:50:00,119 --> 00:50:07,119
	um another important topic is code

	1176
	00:50:03,359 --> 00:50:08,880
	synthesis from input output examples so

	1177
	00:50:07,119 --> 00:50:12,319
	actually when you said code generation

	1178
	00:50:08,880 --> 00:50:14,760
	or code synthesis like five years ago or

	1179
	00:50:12,319 --> 00:50:17,440
	10 years ago a lot of people would think

	1180
	00:50:14,760 --> 00:50:19,440
	about this uh so this is actually this

	1181
	00:50:17,440 --> 00:50:22,440
	has been around a lot longer than code

	1182
	00:50:19,440 --> 00:50:24,160
	synthesis um than serious inquiries into

	1183
	00:50:22,440 --> 00:50:27,680
	code synthesis from natural

	1184
	00:50:24,160 --> 00:50:30,680
	language um

	1185
	00:50:27,680 --> 00:50:33,839
	so basically the way this works is it

	1186
	00:50:30,680 --> 00:50:35,319
	can have no natural language whatsoever

	1187
	00:50:33,839 --> 00:50:39,119
	um but you still can try to guess the

	1188
	00:50:35,319 --> 00:50:42,000
	input from uh input output examples when

	1189
	00:50:39,119 --> 00:50:44,319
	would you want to do this so one example

	1190
	00:50:42,000 --> 00:50:45,839
	of this is something called flashfill

	1191
	00:50:44,319 --> 00:50:48,599
	which has been around for a very long

	1192
	00:50:45,839 --> 00:50:51,839
	time in Microsoft Excel and basically

	1193
	00:50:48,599 --> 00:50:55,400
	the way it works is you have one column

	1194
	00:50:51,839 --> 00:50:58,640
	and um the column might be

	1195
	00:50:55,400 --> 00:50:58,640
	like uh

	1196
	00:50:59,559 --> 00:51:02,880
	R new

	1197
	00:51:03,040 --> 00:51:12,799
	big and uh

	1198
	00:51:06,559 --> 00:51:12,799
	else just pick on three because he also

	1199
	00:51:14,040 --> 00:51:19,599
	up and so we have this column and then

	1200
	00:51:17,160 --> 00:51:19,599
	we have like

	1201
	00:51:20,400 --> 00:51:26,760
	gig um and from like one or a couple

	1202
	00:51:25,160 --> 00:51:28,400
	examples basically what it does is it

	1203
	00:51:26,760 --> 00:51:30,319
	tries to induce a program that can

	1204
	00:51:28,400 --> 00:51:33,319
	generate all the other examples properly

	1205
	00:51:30,319 --> 00:51:35,599
	so in this particular case that would be

	1206
	00:51:33,319 --> 00:51:38,440
	um you know like

	1207
	00:51:35,599 --> 00:51:40,480
	split take the first character from the

	1208
	00:51:38,440 --> 00:51:43,280
	first one and all of the last one and

	1209
	00:51:40,480 --> 00:51:45,280
	then concatenate and then M or something

	1210
	00:51:43,280 --> 00:51:48,280
	like that right

	1211
	00:51:45,280 --> 00:51:50,079
	um and so this is useful in some cases

	1212
	00:51:48,280 --> 00:51:51,599
	like you know in Excel when you have

	1213
	00:51:50,079 --> 00:51:53,359
	this long sheet and you want to fill in

	1214
	00:51:51,599 --> 00:51:56,160
	the rest of it and this has actually

	1215
	00:51:53,359 --> 00:51:57,720
	been deployed uh you know in Excel in

	1216
	00:51:56,160 --> 00:52:00,960
	white

	1217
	00:51:57,720 --> 00:52:02,559
	used um if you're interested in this

	1218
	00:52:00,960 --> 00:52:06,040
	topic there's a fair amount of work in

	1219
	00:52:02,559 --> 00:52:08,839
	it um my there's a little bit less work

	1220
	00:52:06,040 --> 00:52:10,240
	now because most people are focusing on

	1221
	00:52:08,839 --> 00:52:12,400
	uh learning programs from natural

	1222
	00:52:10,240 --> 00:52:14,839
	language and other stuff like this but

	1223
	00:52:12,400 --> 00:52:16,480
	uh this slightly older Pap paper called

	1224
	00:52:14,839 --> 00:52:19,359
	interpret explains a bunch of the

	1225
	00:52:16,480 --> 00:52:22,880
	different methods that people used and

	1226
	00:52:19,359 --> 00:52:25,920
	um how you uh like how they compare and

	1227
	00:52:22,880 --> 00:52:28,119
	stuff and also um Joshua ten and bums

	1228
	00:52:25,920 --> 00:52:29,880
	group from MI has done a lot on program

	1229
	00:52:28,119 --> 00:52:31,319
	synthesis from input output examples so

	1230
	00:52:29,880 --> 00:52:32,359
	you could also take a look at that that

	1231
	00:52:31,319 --> 00:52:35,079
	sounds

	1232
	00:52:32,359 --> 00:52:38,240
	interesting um one thing about this is

	1233
	00:52:35,079 --> 00:52:40,280
	these generally are mostly done on

	1234
	00:52:38,240 --> 00:52:43,319
	domain specific languages so they're

	1235
	00:52:40,280 --> 00:52:46,839
	mostly done like only for reg X's or

	1236
	00:52:43,319 --> 00:52:48,480
	they're done only for you know SQL or

	1237
	00:52:46,839 --> 00:52:50,079
	something like that not for the more

	1238
	00:52:48,480 --> 00:52:51,960
	general purpose languages just because

	1239
	00:52:50,079 --> 00:52:54,079
	the problem without any natural language

	1240
	00:52:51,960 --> 00:52:56,520
	specification is harder and so you need

	1241
	00:52:54,079 --> 00:52:57,520
	to like make the search space smaller or

	1242
	00:52:56,520 --> 00:53:01,559
	Additionally you needed to make the

	1243
	00:52:57,520 --> 00:53:04,440
	search small for theable so um that's a

	1244
	00:53:01,559 --> 00:53:04,440
	another thing to know

	1245
	00:53:04,799 --> 00:53:09,440
	about cool um any questions about

	1246
	00:53:09,480 --> 00:53:14,440
	these nice okay so finally in the the

	1247
	00:53:12,559 --> 00:53:15,599
	last few minutes I'd like to talk about

	1248
	00:53:14,440 --> 00:53:18,480
	um code

	1249
	00:53:15,599 --> 00:53:22,880
	LMS and I'm going to go through about

	1250
	00:53:18,480 --> 00:53:24,599
	four of them the first one is codex and

	1251
	00:53:22,880 --> 00:53:26,200
	so yeah actually what I should mention

	1252
	00:53:24,599 --> 00:53:28,079
	is all of the LMS that I talked about up

	1253
	00:53:26,200 --> 00:53:30,640
	until this point are code LMS because

	1254
	00:53:28,079 --> 00:53:31,680
	every LM trains on code so I'm mainly

	1255
	00:53:30,640 --> 00:53:36,119
	going to be talking about one

	1256
	00:53:31,680 --> 00:53:39,200
	specifically for code this time um so

	1257
	00:53:36,119 --> 00:53:42,480
	codex is the first and kind of like

	1258
	00:53:39,200 --> 00:53:45,880
	first really big impact Cod LM um it was

	1259
	00:53:42,480 --> 00:53:47,720
	created by open AI um originally I don't

	1260
	00:53:45,880 --> 00:53:49,079
	know about the deployed model now

	1261
	00:53:47,720 --> 00:53:51,599
	because you know they don't release the

	1262
	00:53:49,079 --> 00:53:53,799
	details of it but originally this was

	1263
	00:53:51,599 --> 00:53:57,920
	trained by continued training from

	1264
	00:53:53,799 --> 00:53:59,799
	gpt3 so they had a text M and then they

	1265
	00:53:57,920 --> 00:54:03,079
	just continued training it on lots and

	1266
	00:53:59,799 --> 00:54:05,680
	lots of code from GitHub um so yeah the

	1267
	00:54:03,079 --> 00:54:08,799
	data was lots of data from GitHub um if

	1268
	00:54:05,680 --> 00:54:11,280
	you did anything on GitHub at any point

	1269
	00:54:08,799 --> 00:54:14,119
	in your life uh you might be uh

	1270
	00:54:11,280 --> 00:54:17,720
	contributing to codep so thank you on

	1271
	00:54:14,119 --> 00:54:22,440
	behalf of open AI a 80 billion dollar

	1272
	00:54:17,720 --> 00:54:24,599
	company and uh importantly it Powers I

	1273
	00:54:22,440 --> 00:54:27,599
	believe it still Powers GitHub

	1274
	00:54:24,599 --> 00:54:31,160
	co-pilot one interesting thing is they

	1275
	00:54:27,599 --> 00:54:33,119
	had a large version of codex um and then

	1276
	00:54:31,160 --> 00:54:35,799
	they had a smaller version of codex

	1277
	00:54:33,119 --> 00:54:38,359
	called code kushman and the thing

	1278
	00:54:35,799 --> 00:54:40,040
	actually powering GitHub co-pilot is not

	1279
	00:54:38,359 --> 00:54:42,839
	the the largest version it's not code Da

	1280
	00:54:40,040 --> 00:54:46,359
	Vinci it's code kushman which is uh

	1281
	00:54:42,839 --> 00:54:48,680
	smaller and much faster and the reason

	1282
	00:54:46,359 --> 00:54:50,640
	why is probably twofold number one um

	1283
	00:54:48,680 --> 00:54:54,160
	you need really fast responses when

	1284
	00:54:50,640 --> 00:54:55,760
	you're you know working on code and

	1285
	00:54:54,160 --> 00:54:57,440
	there's actually in co-pilot there's

	1286
	00:54:55,760 --> 00:55:00,280
	some cach and other stuff like that to

	1287
	00:54:57,440 --> 00:55:01,960
	make your responses very fast as well um

	1288
	00:55:00,280 --> 00:55:03,400
	the second reason is probably it' just

	1289
	00:55:01,960 --> 00:55:05,040
	be too expensive for them to run Da

	1290
	00:55:03,400 --> 00:55:06,760
	Vinci over all the code bases for how

	1291
	00:55:05,040 --> 00:55:10,400
	much they're charging you for co-pilot

	1292
	00:55:06,760 --> 00:55:12,119
	so like every single time you like

	1293
	00:55:10,400 --> 00:55:14,280
	change something in one of your files if

	1294
	00:55:12,119 --> 00:55:17,079
	you're using copilot it's rerunning in

	1295
	00:55:14,280 --> 00:55:19,359
	llm and that would become very expensive

	1296
	00:55:17,079 --> 00:55:20,599
	if you look look at the token count so I

	1297
	00:55:19,359 --> 00:55:21,839
	think they're using a smaller model

	1298
	00:55:20,599 --> 00:55:22,920
	because of that but nonetheless it's

	1299
	00:55:21,839 --> 00:55:27,039
	very

	1300
	00:55:22,920 --> 00:55:28,640
	good um cool

	1301
	00:55:27,039 --> 00:55:30,680
	so now I want to get into some more

	1302
	00:55:28,640 --> 00:55:33,880
	modern models uh the first one I want to

	1303
	00:55:30,680 --> 00:55:35,520
	get into is uh star coder 2 and the

	1304
	00:55:33,880 --> 00:55:38,359
	reason why I want to talk about this

	1305
	00:55:35,520 --> 00:55:40,160
	first is because uh not necessarily that

	1306
	00:55:38,359 --> 00:55:41,880
	it's like absolutely the best one

	1307
	00:55:40,160 --> 00:55:43,400
	although it's very good but it's one of

	1308
	00:55:41,880 --> 00:55:45,319
	the models that actually tells us

	1309
	00:55:43,400 --> 00:55:47,240
	everything about their training data and

	1310
	00:55:45,319 --> 00:55:50,400
	training process and stuff so we know uh

	1311
	00:55:47,240 --> 00:55:53,039
	everything about them so the creator of

	1312
	00:55:50,400 --> 00:55:54,440
	This was um the big science project

	1313
	00:55:53,039 --> 00:55:56,880
	which was led by hugging face and

	1314
	00:55:54,440 --> 00:55:58,680
	service now um

	1315
	00:55:56,880 --> 00:56:02,079
	and includes lots and lots of people

	1316
	00:55:58,680 --> 00:56:04,960
	from various universities and things um

	1317
	00:56:02,079 --> 00:56:09,319
	the architecture is mostly llama style

	1318
	00:56:04,960 --> 00:56:11,960
	it has 3B 7B and 15b variants um one

	1319
	00:56:09,319 --> 00:56:15,480
	interesting thing about all code LMS is

	1320
	00:56:11,960 --> 00:56:17,680
	that they all do long context they all

	1321
	00:56:15,480 --> 00:56:20,359
	do longer context and they all

	1322
	00:56:17,680 --> 00:56:23,200
	reconfigure rope for longer context

	1323
	00:56:20,359 --> 00:56:25,280
	specifically so you know rope has a

	1324
	00:56:23,200 --> 00:56:28,599
	Theta parameter that allows you to tell

	1325
	00:56:25,280 --> 00:56:31,720
	how long the um like sign sine waves and

	1326
	00:56:28,599 --> 00:56:33,720
	stuff like that are and they all always

	1327
	00:56:31,720 --> 00:56:36,079
	um change the parameters so that the

	1328
	00:56:33,720 --> 00:56:38,599
	context is longer so that's another good

	1329
	00:56:36,079 --> 00:56:38,599
	thing to know

	1330
	00:56:38,640 --> 00:56:44,559
	about the the training data section of

	1331
	00:56:42,000 --> 00:56:48,799
	this paper is really fascinating I can

	1332
	00:56:44,559 --> 00:56:51,240
	like it it's a really good way to look

	1333
	00:56:48,799 --> 00:56:54,160
	at you know how much data engineering

	1334
	00:56:51,240 --> 00:56:55,960
	goes into making a good model um and

	1335
	00:56:54,160 --> 00:56:57,960
	just very shortly they give a lot more

	1336
	00:56:55,960 --> 00:57:00,640
	detail in the paper but it's trained on

	1337
	00:56:57,960 --> 00:57:04,839
	code uh including the stack which is

	1338
	00:57:00,640 --> 00:57:06,920
	just a huge uh amount like repository of

	1339
	00:57:04,839 --> 00:57:08,359
	code that I'll talk about in a second

	1340
	00:57:06,920 --> 00:57:10,559
	separately from that it was trained on

	1341
	00:57:08,359 --> 00:57:13,079
	GitHub issues it was trained on poll

	1342
	00:57:10,559 --> 00:57:16,000
	requests Jupiter notebooks keggle

	1343
	00:57:13,079 --> 00:57:18,319
	notebooks documentation and also

	1344
	00:57:16,000 --> 00:57:23,440
	intermediate representations from uh

	1345
	00:57:18,319 --> 00:57:26,440
	llvm so llvm is a uh you know like

	1346
	00:57:23,440 --> 00:57:28,920
	intermediate uh compiler style thing

	1347
	00:57:26,440 --> 00:57:30,839
	that is used for compiling code and it

	1348
	00:57:28,920 --> 00:57:34,400
	was also trained on a few code relevant

	1349
	00:57:30,839 --> 00:57:38,440
	natural language data sets

	1350
	00:57:34,400 --> 00:57:39,960
	um so for pre-processing they do

	1351
	00:57:38,440 --> 00:57:42,640
	something pretty interesting which is

	1352
	00:57:39,960 --> 00:57:44,240
	they add metadata tags such as the repo

	1353
	00:57:42,640 --> 00:57:48,119
	name and the file name and other stuff

	1354
	00:57:44,240 --> 00:57:49,799
	like this uh 50% of the time and they do

	1355
	00:57:48,119 --> 00:57:51,599
	this 50% of the time because they want

	1356
	00:57:49,799 --> 00:57:54,400
	the model to work with them but also be

	1357
	00:57:51,599 --> 00:57:57,079
	robust without them um and so you can

	1358
	00:57:54,400 --> 00:57:59,839
	either add them or not add them at test

	1359
	00:57:57,079 --> 00:58:03,079
	time uh they also do infilling every

	1360
	00:57:59,839 --> 00:58:05,960
	serus code LM does infilling Based

	1361
	00:58:03,079 --> 00:58:07,480
	training um one interesting thing about

	1362
	00:58:05,960 --> 00:58:08,960
	this from the training perspective is

	1363
	00:58:07,480 --> 00:58:12,000
	they actually trained it for four to

	1364
	00:58:08,960 --> 00:58:14,359
	five epochs um which is much more than

	1365
	00:58:12,000 --> 00:58:17,160
	we normally do so normally we only train

	1366
	00:58:14,359 --> 00:58:18,359
	for like one Epoch over you know all of

	1367
	00:58:17,160 --> 00:58:20,079
	the data we have but here they were

	1368
	00:58:18,359 --> 00:58:21,319
	training for monger and that's just

	1369
	00:58:20,079 --> 00:58:23,359
	because the amount of data they can get

	1370
	00:58:21,319 --> 00:58:24,400
	for code is less than the amount of data

	1371
	00:58:23,359 --> 00:58:27,200
	they can get for all the national

	1372
	00:58:24,400 --> 00:58:30,039
	language I

	1373
	00:58:27,200 --> 00:58:33,200
	so the data set that they created is uh

	1374
	00:58:30,039 --> 00:58:36,119
	the stack 2 and this is a code

	1375
	00:58:33,200 --> 00:58:37,839
	pre-training data set um one interesting

	1376
	00:58:36,119 --> 00:58:40,039
	thing that they thought about was uh

	1377
	00:58:37,839 --> 00:58:42,960
	license considerations so I talked about

	1378
	00:58:40,039 --> 00:58:44,480
	the um how copyright is a problem when

	1379
	00:58:42,960 --> 00:58:46,640
	trading large language models two

	1380
	00:58:44,480 --> 00:58:48,880
	classes ago and so here they

	1381
	00:58:46,640 --> 00:58:50,119
	specifically tried to find things with

	1382
	00:58:48,880 --> 00:58:52,520
	permissive

	1383
	00:58:50,119 --> 00:58:53,880
	licenses and so what they did is they

	1384
	00:58:52,520 --> 00:58:57,000
	basically looked at the license on

	1385
	00:58:53,880 --> 00:58:59,520
	GitHub um and if the GitHub license was

	1386
	00:58:57,000 --> 00:59:01,440
	permissive they marked it as permissive

	1387
	00:58:59,520 --> 00:59:02,880
	um then they tried to detect licenses

	1388
	00:59:01,440 --> 00:59:05,720
	and then um if all of them were

	1389
	00:59:02,880 --> 00:59:08,000
	permissive they marked it as

	1390
	00:59:05,720 --> 00:59:10,480
	permissive this is a huge table that

	1391
	00:59:08,000 --> 00:59:14,160
	they have in the paper of all of the

	1392
	00:59:10,480 --> 00:59:15,480
	data that they have and um you know I'm

	1393
	00:59:14,160 --> 00:59:16,920
	not going to go through all of this

	1394
	00:59:15,480 --> 00:59:18,920
	obviously but what you can see is some

	1395
	00:59:16,920 --> 00:59:22,480
	of the biggest data sets are like

	1396
	00:59:18,920 --> 00:59:26,280
	Java um

	1397
	00:59:22,480 --> 00:59:28,640
	PHP markdown

	1398
	00:59:26,280 --> 00:59:30,039
	and uh Python and other stuff like that

	1399
	00:59:28,640 --> 00:59:32,240
	so you can see the major programming

	1400
	00:59:30,039 --> 00:59:35,559
	languages have lots of data but there's

	1401
	00:59:32,240 --> 00:59:38,400
	also a long tail so if you like your uh

	1402
	00:59:35,559 --> 00:59:40,440
	you know more esoteric uh but cool

	1403
	00:59:38,400 --> 00:59:43,960
	programming languages like rust yes it

	1404
	00:59:40,440 --> 00:59:46,160
	has rust too so um we can do all all of

	1405
	00:59:43,960 --> 00:59:46,160
	those

	1406
	00:59:46,480 --> 00:59:53,079
	things so the next model that I'd like

	1407
	00:59:49,799 --> 00:59:55,200
	to talk about is cod llama and cod llama

	1408
	00:59:53,079 --> 00:59:57,920
	is another competitive model it came out

	1409
	00:59:55,200 --> 00:59:59,480
	a little bit before star coder and star

	1410
	00:59:57,920 --> 01:00:02,680
	coder 2 and deep sea coder which I'm

	1411
	00:59:59,480 --> 01:00:04,079
	going to talk about um this is a created

	1412
	01:00:02,680 --> 01:00:08,319
	by

	1413
	01:00:04,079 --> 01:00:11,160
	meta and um the architecture is the same

	1414
	01:00:08,319 --> 01:00:14,280
	as llama 2 uh basically and they did

	1415
	01:00:11,160 --> 01:00:16,400
	continued training from llama 2 um but

	1416
	01:00:14,280 --> 01:00:18,000
	they trained it on longer input contexts

	1417
	01:00:16,400 --> 01:00:21,720
	and they also extended the length of

	1418
	01:00:18,000 --> 01:00:23,559
	rope so uh those are you know standard

	1419
	01:00:21,720 --> 01:00:26,680
	things for code language

	1420
	01:00:23,559 --> 01:00:28,680
	models it was trained on DED code and

	1421
	01:00:26,680 --> 01:00:30,400
	also synthetically created instruction

	1422
	01:00:28,680 --> 01:00:33,280
	data so they created like instruction

	1423
	01:00:30,400 --> 01:00:37,920
	tuning data specifically for

	1424
	01:00:33,280 --> 01:00:39,480
	code um and the training was incremental

	1425
	01:00:37,920 --> 01:00:42,559
	with various data sets and what I mean

	1426
	01:00:39,480 --> 01:00:45,599
	by this is they trained on 500 billion

	1427
	01:00:42,559 --> 01:00:47,599
	uh I believe tokens of code and then

	1428
	01:00:45,599 --> 01:00:50,400
	they did long context fine tuning on 20

	1429
	01:00:47,599 --> 01:00:52,599
	billion tokens and then they also did

	1430
	01:00:50,400 --> 01:00:55,400
	instruction tuning they also have a

	1431
	01:00:52,599 --> 01:00:57,079
	python specific one and the reason why

	1432
	01:00:55,400 --> 01:00:59,640
	they have a p specific one is not

	1433
	01:00:57,079 --> 01:01:02,319
	because python is more import important

	1434
	01:00:59,640 --> 01:01:03,839
	uh uh necessarily but because a lot of

	1435
	01:01:02,319 --> 01:01:05,559
	the benchmarks are in Python because

	1436
	01:01:03,839 --> 01:01:06,920
	machine learning people like who are

	1437
	01:01:05,559 --> 01:01:09,240
	creating benchmarks they also like

	1438
	01:01:06,920 --> 01:01:11,200
	python so python is more common in the

	1439
	01:01:09,240 --> 01:01:14,240
	benchmarks so they basically wanted to

	1440
	01:01:11,200 --> 01:01:15,720
	do well on the benchmarks I think uh and

	1441
	01:01:14,240 --> 01:01:17,920
	and created a data set that does well in

	1442
	01:01:15,720 --> 01:01:19,240
	the benchmarks but um if you are

	1443
	01:01:17,920 --> 01:01:23,160
	creating python you can use the code

	1444
	01:01:19,240 --> 01:01:25,280
	llama python it's better at pipelines so

	1445
	01:01:23,160 --> 01:01:28,000
	um and then the final one I'd like to

	1446
	01:01:25,280 --> 01:01:29,839
	talk about is is a deep seek coder uh

	1447
	01:01:28,000 --> 01:01:32,079
	this is notable because it's a very

	1448
	01:01:29,839 --> 01:01:34,599
	strong model it it's maybe the strongest

	1449
	01:01:32,079 --> 01:01:38,799
	model on average over all the code

	1450
	01:01:34,599 --> 01:01:41,599
	models um they did 87% the data is not

	1451
	01:01:38,799 --> 01:01:44,640
	super clear but they did 87% source code

	1452
	01:01:41,599 --> 01:01:46,359
	10% English um from markdown in stock

	1453
	01:01:44,640 --> 01:01:51,160
	exchange and 3% Chinese because it's

	1454
	01:01:46,359 --> 01:01:53,559
	from a Chinese company deep seek um and

	1455
	01:01:51,160 --> 01:01:54,960
	they did standard prepr uh but one

	1456
	01:01:53,559 --> 01:01:57,319
	interesting thing they did is they

	1457
	01:01:54,960 --> 01:01:59,200
	included Library dependencies so they

	1458
	01:01:57,319 --> 01:02:01,799
	basically crawled the dependency graph

	1459
	01:01:59,200 --> 01:02:03,640
	of libraries pulled out files from the

	1460
	01:02:01,799 --> 01:02:06,000
	libraries that were referenced and then

	1461
	01:02:03,640 --> 01:02:07,440
	used them in training and so that's

	1462
	01:02:06,000 --> 01:02:09,319
	particularly useful if you want the

	1463
	01:02:07,440 --> 01:02:12,920
	model to be able to reference external

	1464
	01:02:09,319 --> 01:02:14,039
	libraries well um so that's kind of an

	1465
	01:02:12,920 --> 01:02:17,279
	interesting

	1466
	01:02:14,039 --> 01:02:19,599
	thing um the architecture is pretty

	1467
	01:02:17,279 --> 01:02:22,960
	standard it's llama likee with 1.3

	1468
	01:02:19,599 --> 01:02:24,599
	billion 6.7 billion in 33b variants and

	1469
	01:02:22,960 --> 01:02:27,279
	it has a reconfigured work like the

	1470
	01:02:24,599 --> 01:02:30,520
	others and they on two trillion

	1471
	01:02:27,279 --> 01:02:34,200
	tokens um so then a question becomes

	1472
	01:02:30,520 --> 01:02:36,680
	which one to use um and I created a

	1473
	01:02:34,200 --> 01:02:39,160
	summary here um all of them have

	1474
	01:02:36,680 --> 01:02:40,760
	somewhat similar performance uh this is

	1475
	01:02:39,160 --> 01:02:42,760
	they're compared in the star coder 2

	1476
	01:02:40,760 --> 01:02:45,640
	paper so you can go in and look at

	1477
	01:02:42,760 --> 01:02:48,160
	details at the starcode to paper um

	1478
	01:02:45,640 --> 01:02:51,119
	deeps coder seems to be strong on

	1479
	01:02:48,160 --> 01:02:52,799
	standard programming tasks um whereas

	1480
	01:02:51,119 --> 01:02:54,799
	star coder seems to be strong on data

	1481
	01:02:52,799 --> 01:02:56,680
	science notebooks so like on average

	1482
	01:02:54,799 --> 01:02:59,160
	it's better at kind of sound notebooks

	1483
	01:02:56,680 --> 01:03:02,079
	but all of them are good models um all

	1484
	01:02:59,160 --> 01:03:05,440
	of them are not quite as good as uh like

	1485
	01:03:02,079 --> 01:03:08,920
	gp4 quad on like they're very uh you

	1486
	01:03:05,440 --> 01:03:10,799
	know more complex tasks but uh they're

	1487
	01:03:08,920 --> 01:03:12,359
	available and you can find to them and

	1488
	01:03:10,799 --> 01:03:16,880
	do other things like that as

	1489
	01:03:12,359 --> 01:03:21,599
	well one caveat about the Deep seek

	1490
	01:03:16,880 --> 01:03:24,640
	thing is actually if I go back to this

	1491
	01:03:21,599 --> 01:03:27,559
	slide um a lot of the models up here are

	1492
	01:03:24,640 --> 01:03:29,640
	deep seek um so you do need to be a

	1493
	01:03:27,559 --> 01:03:31,400
	little bit careful about like

	1494
	01:03:29,640 --> 01:03:34,400
	interpreting their human Evel results

	1495
	01:03:31,400 --> 01:03:36,319
	because it's possible that the model uh

	1496
	01:03:34,400 --> 01:03:38,799
	was trained on data very similar to

	1497
	01:03:36,319 --> 01:03:40,279
	human eval or something like that so do

	1498
	01:03:38,799 --> 01:03:42,880
	take that with a grain of salt but even

	1499
	01:03:40,279 --> 01:03:44,520
	on other data sets where presumably the

	1500
	01:03:42,880 --> 01:03:46,760
	model has not seen those data sets it

	1501
	01:03:44,520 --> 01:03:49,920
	still does very well so it's not like

	1502
	01:03:46,760 --> 01:03:51,480
	it's um you know as you can see it's

	1503
	01:03:49,920 --> 01:03:54,640
	still one of the most competitive code

	1504
	01:03:51,480 --> 01:03:57,680
	models even on this new LCB um data set

	1505
	01:03:54,640 --> 01:04:01,359
	so uh that's want into the

	1506
	01:03:57,680 --> 01:04:03,000
	a cool um that's all I have for today I

	1507
	01:04:01,359 --> 01:04:04,359
	you know I love to talk about this topic

	1508
	01:04:03,000 --> 01:04:06,480
	I've done a lot of research on it so I'm

	1509
	01:04:04,359 --> 01:04:11,200
	happy to discuss any questions if people

	1510
	01:04:06,480 --> 01:04:14,720
	have them either in front of everyone or

	1511
	01:04:11,200 --> 01:04:14,720
	after any any

	1512
	01:04:16,480 --> 01:04:24,160
	questions uh yeah just wondering there a

	1513
	01:04:20,359 --> 01:04:27,720
	like enfor the outut during using things

	1514
	01:04:24,160 --> 01:04:27,720
	other than models

	1515
	01:04:30,599 --> 01:04:36,599
	yeah great question is there a way to

	1516
	01:04:33,640 --> 01:04:38,200
	enforce uh restrictions at decoding time

	1517
	01:04:36,599 --> 01:04:39,760
	other than using the model's uh

	1518
	01:04:38,200 --> 01:04:42,240
	probabilities because this is code and

	1519
	01:04:39,760 --> 01:04:42,240
	we know the

	1520
	01:04:42,440 --> 01:04:51,079
	syntax yes and no um there

	1521
	01:04:46,319 --> 01:04:53,200
	are for code it's not always immediately

	1522
	01:04:51,079 --> 01:04:54,400
	obvious like I mean one one thing you

	1523
	01:04:53,200 --> 01:04:55,960
	could do is just generate a bunch of

	1524
	01:04:54,400 --> 01:04:58,520
	results and throw out all the syntax

	1525
	01:04:55,960 --> 01:04:59,480
	incorrect on that's easy right um but if

	1526
	01:04:58,520 --> 01:05:02,520
	you don't want to do that and you want

	1527
	01:04:59,480 --> 01:05:04,839
	to do it at decoding time it's dependent

	1528
	01:05:02,520 --> 01:05:07,480
	on you being able to have an incremental

	1529
	01:05:04,839 --> 01:05:09,079
	syntax parser that allows you to like

	1530
	01:05:07,480 --> 01:05:12,400
	throw out bad

	1531
	01:05:09,079 --> 01:05:14,160
	hypotheses like incrementally and that's

	1532
	01:05:12,400 --> 01:05:16,240
	possible that's very easy for some

	1533
	01:05:14,160 --> 01:05:17,200
	languages and not possible not as easy

	1534
	01:05:16,240 --> 01:05:20,559
	for other

	1535
	01:05:17,200 --> 01:05:23,720
	languages um one really big thing right

	1536
	01:05:20,559 --> 01:05:26,599
	now is Json so like a lot of the time

	1537
	01:05:23,720 --> 01:05:28,319
	people want to Output Json uh in you

	1538
	01:05:26,599 --> 01:05:31,559
	know then par the Json and use it in

	1539
	01:05:28,319 --> 01:05:36,640
	some Downstream test and there actually

	1540
	01:05:31,559 --> 01:05:36,640
	are libraries um just to give a

	1541
	01:05:38,559 --> 01:05:45,839
	few um here's one this Library called

	1542
	01:05:42,640 --> 01:05:48,799
	outlines um is one that basically allows

	1543
	01:05:45,839 --> 01:05:50,440
	you to incorporate syntactic constraints

	1544
	01:05:48,799 --> 01:05:53,240
	through like weighted finite State

	1545
	01:05:50,440 --> 01:05:55,160
	automata and other stuff like this um to

	1546
	01:05:53,240 --> 01:05:57,680
	allow you to throw away anything that

	1547
	01:05:55,160 --> 01:06:02,039
	doesn't here to your grammar another

	1548
	01:05:57,680 --> 01:06:02,039
	popular one which

	1549
	01:06:02,720 --> 01:06:06,880
	is nice but a little bit more

	1550
	01:06:07,160 --> 01:06:12,760
	complicated is

	1551
	01:06:09,799 --> 01:06:15,160
	um this one uh

	1552
	01:06:12,760 --> 01:06:17,200
	guidance so if you want to look at like

	1553
	01:06:15,160 --> 01:06:19,720
	constrained generation of outputs I

	1554
	01:06:17,200 --> 01:06:21,640
	would definitely recommend uh looking at

	1555
	01:06:19,720 --> 01:06:22,839
	one of these two either outlines or or

	1556
	01:06:21,640 --> 01:06:24,440
	guidance and they both give you

	1557
	01:06:22,839 --> 01:06:26,520
	different ways to add constraints to

	1558
	01:06:24,440 --> 01:06:29,079
	Output um we did actually talk about

	1559
	01:06:26,520 --> 01:06:31,200
	outlines a little bit during the like uh

	1560
	01:06:29,079 --> 01:06:34,599
	generation class but um we didn't go

	1561
	01:06:31,200 --> 01:06:35,760
	into a lot of details so uh yeah but I I

	1562
	01:06:34,599 --> 01:06:39,559
	would recommend

	1563
	01:06:35,760 --> 01:06:39,559
	this cool any other

	1564
	01:06:39,599 --> 01:06:43,920
	questions okay if not uh I guess we can

	1565
	01:06:42,079 --> 01:06:47,880
	finish up and I'm happy to talk we have

	1566
	01:06:43,920 --> 01:06:47,880
	a little bit of extra time