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WEBVTT

00:00:00.040 --> 00:00:03.880
so today I'm going to talk about

00:00:01.319 --> 00:00:06.680
retrieval and retrieval augmented

00:00:03.880 --> 00:00:09.040
generation so if we look at our standard

00:00:06.680 --> 00:00:10.880
prompting flow normally what we do is we

00:00:09.040 --> 00:00:14.160
combine together a prompt template with

00:00:10.880 --> 00:00:16.600
an input so if we say please answer this

00:00:14.160 --> 00:00:18.720
question I think Vin Diesel has been a

00:00:16.600 --> 00:00:21.000
voice actor for several pictors in TV

00:00:18.720 --> 00:00:24.000
series do you know what their names

00:00:21.000 --> 00:00:25.400
are we could get a response from a

00:00:24.000 --> 00:00:26.840
language model but there are several

00:00:25.400 --> 00:00:30.840
problems with

00:00:26.840 --> 00:00:33.680
this the first is accuracy issues

00:00:30.840 --> 00:00:36.160
the models generally have a knowledge

00:00:33.680 --> 00:00:38.879
cut off so the parameters are usually

00:00:36.160 --> 00:00:41.120
only updated to a particular time so for

00:00:38.879 --> 00:00:43.200
example if a new Vin Diesel TV series

00:00:41.120 --> 00:00:44.960
comes out then the model that was

00:00:43.200 --> 00:00:47.440
trained up to a certain time Point won't

00:00:44.960 --> 00:00:51.000
be able to know anything about

00:00:47.440 --> 00:00:53.600
it there's also issues of private data

00:00:51.000 --> 00:00:55.320
so data stored in private text or data

00:00:53.600 --> 00:00:57.840
repositories is not suitable for

00:00:55.320 --> 00:01:02.600
training for a number of reasons number

00:00:57.840 --> 00:01:05.199
one it's not available to to particular

00:01:02.600 --> 00:01:07.799
language model training providers such

00:01:05.199 --> 00:01:10.720
as you know open AI or Google or anybody

00:01:07.799 --> 00:01:13.840
else like this the second thing is

00:01:10.720 --> 00:01:16.799
Access Control issues so even if you're

00:01:13.840 --> 00:01:17.840
within an organization that has lots of

00:01:16.799 --> 00:01:20.799
private data and you can train a

00:01:17.840 --> 00:01:22.600
language model on that certain people in

00:01:20.799 --> 00:01:24.200
the organization may have access to

00:01:22.600 --> 00:01:27.640
certain varieties of data and other

00:01:24.200 --> 00:01:29.400
people may not so it's not just solely

00:01:27.640 --> 00:01:31.520
an issue of third party providers it's

00:01:29.400 --> 00:01:33.840
an issue of organization level Access

00:01:31.520 --> 00:01:36.159
Control in

00:01:33.840 --> 00:01:38.920
general in addition there are learning

00:01:36.159 --> 00:01:40.320
failures so even for data that the model

00:01:38.920 --> 00:01:42.640
was trained on it might not be

00:01:40.320 --> 00:01:44.399
sufficient to get the right answer and

00:01:42.640 --> 00:01:47.799
this is particularly the case for very

00:01:44.399 --> 00:01:52.320
very large uh training data sets and

00:01:47.799 --> 00:01:53.920
models that are you know modestly sized

00:01:52.320 --> 00:01:55.880
because the models very often won't be

00:01:53.920 --> 00:01:58.360
able to learn from a single look at a

00:01:55.880 --> 00:02:02.039
particular fact or or whatever else like

00:01:58.360 --> 00:02:02.039
this especially if iter early in

00:02:02.159 --> 00:02:08.160
training another thing is even if the

00:02:05.240 --> 00:02:10.599
answer is correct it might not be

00:02:08.160 --> 00:02:13.440
verifiable so you might want to be very

00:02:10.599 --> 00:02:15.000
sure that the model is not making any

00:02:13.440 --> 00:02:17.640
accuracy

00:02:15.000 --> 00:02:19.040
problems and so in order to do that very

00:02:17.640 --> 00:02:21.879
often a human will want to go back to

00:02:19.040 --> 00:02:21.879
the source of the

00:02:22.200 --> 00:02:27.319
data so to solve this there's a method

00:02:25.480 --> 00:02:29.200
called retrieval augmented generation

00:02:27.319 --> 00:02:30.280
which will also be the topic of our

00:02:29.200 --> 00:02:32.599
second assignment

00:02:30.280 --> 00:02:35.680
here and the way it works is you

00:02:32.599 --> 00:02:38.319
retrieve relevant passages

00:02:35.680 --> 00:02:40.680
efficiently ones that kind of entail the

00:02:38.319 --> 00:02:42.480
answer to a question and then read the

00:02:40.680 --> 00:02:46.080
passages to answer the

00:02:42.480 --> 00:02:48.599
query so we have documents like this we

00:02:46.080 --> 00:02:52.360
have a query based on the query we form

00:02:48.599 --> 00:02:55.360
retrieval we get a whole bunch of uh

00:02:52.360 --> 00:02:57.560
passages we do reading and then we get

00:02:55.360 --> 00:02:57.560
the

00:02:58.280 --> 00:03:04.440
answer so this is in fact implemented in

00:03:01.720 --> 00:03:07.599
many or even most uh language modeling

00:03:04.440 --> 00:03:09.840
providers including open AI so to give

00:03:07.599 --> 00:03:11.480
an example I asked the question that I

00:03:09.840 --> 00:03:12.879
just said about Vin Diesel's voice

00:03:11.480 --> 00:03:16.599
acting and TV

00:03:12.879 --> 00:03:19.760
series and Chad GPT gave me an answer

00:03:16.599 --> 00:03:22.440
and you can see that J gpt's answer

00:03:19.760 --> 00:03:24.720
includes several places with quotes um

00:03:22.440 --> 00:03:28.159
they the little blue quotes

00:03:24.720 --> 00:03:30.760
there and if you click on the quote it

00:03:28.159 --> 00:03:33.120
tells you where the information Source

00:03:30.760 --> 00:03:35.000
came from and so this one says behind

00:03:33.120 --> 00:03:37.760
the voice actors been

00:03:35.000 --> 00:03:39.920
Diesel and behind the voice actors TV

00:03:37.760 --> 00:03:42.959
shows Big Mouth V

00:03:39.920 --> 00:03:45.640
diesel now if we look

00:03:42.959 --> 00:03:48.640
closer into this answer we'll see that

00:03:45.640 --> 00:03:49.959
it's not perfect even though it is uh

00:03:48.640 --> 00:03:52.519
performing retrieval augmented

00:03:49.959 --> 00:03:54.840
Generations so for example I only asked

00:03:52.519 --> 00:03:57.200
about TV series but it's giving me lots

00:03:54.840 --> 00:03:59.680
of things about movies where it says

00:03:57.200 --> 00:04:01.319
Groot in Guardians of the Galaxy volume

00:03:59.680 --> 00:04:04.480
3 2023

00:04:01.319 --> 00:04:07.200
movie and in fact uh Vin Diesel was not

00:04:04.480 --> 00:04:10.920
even voicing a character named gut here

00:04:07.200 --> 00:04:13.480
so that's definitely an accuracy

00:04:10.920 --> 00:04:15.079
mistake and separately there's a place

00:04:13.480 --> 00:04:17.639
where it says additionally though the

00:04:15.079 --> 00:04:19.959
website for big mouthless Vin Diesel it

00:04:17.639 --> 00:04:22.040
appears to be a misunderstanding or err

00:04:19.959 --> 00:04:25.360
as Nick croll is credited as the voice

00:04:22.040 --> 00:04:27.800
of Vin Diesel in that show so there

00:04:25.360 --> 00:04:30.039
actually Nick croll was acting as V

00:04:27.800 --> 00:04:32.800
diesel but that's um kind of a

00:04:30.039 --> 00:04:34.600
misunderstanding of the reader model but

00:04:32.800 --> 00:04:36.600
anyway you can get the general idea here

00:04:34.600 --> 00:04:40.199
you can also see that it's not perfect

00:04:36.600 --> 00:04:42.720
even for very strong models like GPD

00:04:40.199 --> 00:04:44.800
4 so now I'd like to go into the actual

00:04:42.720 --> 00:04:46.759
methodology that we use for this uh we

00:04:44.800 --> 00:04:50.360
have retrieval

00:04:46.759 --> 00:04:53.160
methods and for the retrieval methods we

00:04:50.360 --> 00:04:55.160
have uh quite a few different options

00:04:53.160 --> 00:04:57.960
I'm going to go through each one of them

00:04:55.160 --> 00:05:00.960
at a time so sparse retrieval document

00:04:57.960 --> 00:05:04.240
level dense retrieval token level DSE

00:05:00.960 --> 00:05:08.039
retrieval cross- encoder reranking and

00:05:04.240 --> 00:05:09.320
blackbox retrieval so blackbox retrieval

00:05:08.039 --> 00:05:11.280
I'm not really going to go into it a

00:05:09.320 --> 00:05:16.000
whole lot basically this is just asking

00:05:11.280 --> 00:05:17.560
a blackbox search engine to retrieve uh

00:05:16.000 --> 00:05:20.000
you know the relevant context and

00:05:17.560 --> 00:05:22.560
getting the top several results

00:05:20.000 --> 00:05:24.039
nonetheless this is a pretty you know

00:05:22.560 --> 00:05:26.800
reasonable method to do it if you want

00:05:24.039 --> 00:05:29.080
to do search over you know lots of data

00:05:26.800 --> 00:05:32.759
that exists on the internet already and

00:05:29.080 --> 00:05:36.600
that in is what chat jpt does it looks

00:05:32.759 --> 00:05:39.240
up on Bing by generating a query to

00:05:36.600 --> 00:05:41.560
Bing so anyway let's go into the actual

00:05:39.240 --> 00:05:43.840
methods that you develop and control

00:05:41.560 --> 00:05:46.600
yourself so the first one is sparse

00:05:43.840 --> 00:05:48.479
retrieval and the way this works is you

00:05:46.600 --> 00:05:50.440
express the query and document as a

00:05:48.479 --> 00:05:53.680
sparse word frequency Vector usually

00:05:50.440 --> 00:05:58.759
normalized by length and so if I ask uh

00:05:53.680 --> 00:06:01.720
query what is NLP we get a vector where

00:05:58.759 --> 00:06:04.120
each row the vector corresponds to a

00:06:01.720 --> 00:06:07.919
different

00:06:04.120 --> 00:06:12.960
token and we asked what is

00:06:07.919 --> 00:06:16.360
NLP and so uh the places for what NLP

00:06:12.960 --> 00:06:18.199
and is will all have a non-zero value

00:06:16.360 --> 00:06:20.199
and everything else will have a zero

00:06:18.199 --> 00:06:21.720
value and we also normalize by the

00:06:20.199 --> 00:06:24.120
length of vectors so we get something

00:06:21.720 --> 00:06:24.120
like

00:06:24.840 --> 00:06:28.440
333333 then we have a whole bunch of

00:06:26.759 --> 00:06:30.720
documents so the first document says

00:06:28.440 --> 00:06:31.759
what is life can is life someone really

00:06:30.720 --> 00:06:33.960
likes

00:06:31.759 --> 00:06:36.000
candy we also have another one that says

00:06:33.960 --> 00:06:38.360
NLP as an acronym for natural language

00:06:36.000 --> 00:06:39.479
processing so this is a pretty good uh

00:06:38.360 --> 00:06:42.479
you

00:06:39.479 --> 00:06:44.840
know answer to our

00:06:42.479 --> 00:06:48.039
question then we also have I like to do

00:06:44.840 --> 00:06:49.360
good research on NLP which is you know a

00:06:48.039 --> 00:06:51.360
nice sentiment but not a very good

00:06:49.360 --> 00:06:54.400
answer to our question I

00:06:51.360 --> 00:06:59.479
guess so if we look at the vectors here

00:06:54.400 --> 00:07:03.280
we have uh what and candy and is have uh

00:06:59.479 --> 00:07:07.120
a fairly high

00:07:03.280 --> 00:07:12.520
score and we have here NLP and is have a

00:07:07.120 --> 00:07:16.479
high score and NLP has a a nonzero

00:07:12.520 --> 00:07:18.400
score So based on this um we find the

00:07:16.479 --> 00:07:20.560
document similarity with the highest

00:07:18.400 --> 00:07:22.039
inner product or cosine similarity in

00:07:20.560 --> 00:07:24.360
the document

00:07:22.039 --> 00:07:27.000
collection and so if we take the inner

00:07:24.360 --> 00:07:28.759
product between these vectors we

00:07:27.000 --> 00:07:31.280
actually see that the first one got the

00:07:28.759 --> 00:07:34.479
highest score because of its

00:07:31.280 --> 00:07:37.440
relatively High values for the words

00:07:34.479 --> 00:07:37.440
what and

00:07:38.160 --> 00:07:43.759
is

00:07:40.199 --> 00:07:46.720
so as you can see common words like what

00:07:43.759 --> 00:07:49.000
and is can get a high score kind of

00:07:46.720 --> 00:07:51.800
regardless of whether a document is very

00:07:49.000 --> 00:07:53.919
relevant and so one way we can fix this

00:07:51.800 --> 00:07:55.960
is through something called term

00:07:53.919 --> 00:07:59.479
waiting and the way that term waiting

00:07:55.960 --> 00:08:02.680
works is in addition to having this

00:07:59.479 --> 00:08:04.599
Vector that

00:08:02.680 --> 00:08:07.680
calculates

00:08:04.599 --> 00:08:10.680
the frequency within a particular

00:08:07.680 --> 00:08:13.639
document we also have an upweighting

00:08:10.680 --> 00:08:15.599
term that gives higher weight to low

00:08:13.639 --> 00:08:18.199
frequency words because low frequency

00:08:15.599 --> 00:08:20.280
words like NLP tend to be more

00:08:18.199 --> 00:08:22.759
informative about whether the document

00:08:20.280 --> 00:08:25.240
is relevant than high frequency words

00:08:22.759 --> 00:08:27.080
like what it is because these high

00:08:25.240 --> 00:08:31.320
frequency words like what and is Could

00:08:27.080 --> 00:08:34.279
Happen kind of regardless of whether

00:08:31.320 --> 00:08:36.680
the you know document is relevant the

00:08:34.279 --> 00:08:41.800
particular terms the person is asking

00:08:36.680 --> 00:08:44.000
about so one well used and easy to

00:08:41.800 --> 00:08:46.560
understand version of this is uh tfidf

00:08:44.000 --> 00:08:48.839
or term frequency indument

00:08:46.560 --> 00:08:51.200
frequency so the way we Define term

00:08:48.839 --> 00:08:52.959
frequency is exactly what I talked about

00:08:51.200 --> 00:08:56.959
before so it's basically the frequency

00:08:52.959 --> 00:08:59.839
of the term uh T in the document d

00:08:56.959 --> 00:09:01.640
normalized by the total term frequency

00:08:59.839 --> 00:09:03.680
within the document so that that's what

00:09:01.640 --> 00:09:06.800
I already showed in the previous

00:09:03.680 --> 00:09:09.360
slide and then indument frequency is a

00:09:06.800 --> 00:09:13.760
little bit more involved but basically

00:09:09.360 --> 00:09:15.760
the way this works is we have log of the

00:09:13.760 --> 00:09:18.160
total number of documents in the

00:09:15.760 --> 00:09:24.040
collection divided

00:09:18.160 --> 00:09:26.760
by the total number of uh times this

00:09:24.040 --> 00:09:30.279
term appeared in any particular

00:09:26.760 --> 00:09:33.360
document and so if a term appears many

00:09:30.279 --> 00:09:36.120
times in any particular document it will

00:09:33.360 --> 00:09:39.240
have a low IDF score uh one that's close

00:09:36.120 --> 00:09:41.519
to zero but if it rarely appears it will

00:09:39.240 --> 00:09:44.120
have a high IDF score so basically this

00:09:41.519 --> 00:09:45.040
is upweighting our frequent terms and

00:09:44.120 --> 00:09:47.560
then for

00:09:45.040 --> 00:09:51.320
tfidf uh we basically multiply these two

00:09:47.560 --> 00:09:53.120
terms together and we upweight the low

00:09:51.320 --> 00:09:55.640
frequency

00:09:53.120 --> 00:10:00.519
words there's another version of this

00:09:55.640 --> 00:10:03.640
called bm25 that is uh widely used used

00:10:00.519 --> 00:10:05.800
um this is more involved so I'm not

00:10:03.640 --> 00:10:08.120
going to go into all of the details but

00:10:05.800 --> 00:10:12.399
basically if you remember back to the

00:10:08.120 --> 00:10:13.720
lecture on count-based language models

00:10:12.399 --> 00:10:14.880
there were a bunch of smoothing

00:10:13.720 --> 00:10:18.839
techniques for these count-based

00:10:14.880 --> 00:10:21.839
language models and this uses uh kind of

00:10:18.839 --> 00:10:25.839
a m multiplicative additive smoothing

00:10:21.839 --> 00:10:27.160
term to upway things instead of using

00:10:25.839 --> 00:10:30.200
the term

00:10:27.160 --> 00:10:33.399
frequency and uh the actual formula is

00:10:30.200 --> 00:10:37.240
here K and B are kind of

00:10:33.399 --> 00:10:39.360
hyperparameters and um average DL is

00:10:37.240 --> 00:10:40.639
average document length the details of

00:10:39.360 --> 00:10:42.120
this are not really important but

00:10:40.639 --> 00:10:43.800
basically what you should know is that

00:10:42.120 --> 00:10:45.639
this is doing some smoothing on the term

00:10:43.800 --> 00:10:48.240
frequencies and you can look in more

00:10:45.639 --> 00:10:48.240
detail if you're

00:10:49.160 --> 00:10:54.920
interested so now that we have this sort

00:10:52.880 --> 00:10:57.959
of term

00:10:54.920 --> 00:11:00.320
based uh sparse Vector we would like to

00:10:57.959 --> 00:11:03.320
use this to look up relevant documents

00:11:00.320 --> 00:11:06.000
in a collection very quickly because you

00:11:03.320 --> 00:11:08.000
know we might have a collection that's

00:11:06.000 --> 00:11:09.720
extremely large like as large as the

00:11:08.000 --> 00:11:12.320
entire internet like what Google is

00:11:09.720 --> 00:11:14.160
doing when it searches and so in order

00:11:12.320 --> 00:11:16.240
to solve this we need a data structure

00:11:14.160 --> 00:11:17.279
that allows for efficient sparse lookup

00:11:16.240 --> 00:11:19.480
of

00:11:17.279 --> 00:11:23.720
vectors and so we have all of these

00:11:19.480 --> 00:11:27.279
sparse vectors like this

00:11:23.720 --> 00:11:31.240
and we uh basically turn this into an

00:11:27.279 --> 00:11:34.720
index where we have something like a you

00:11:31.240 --> 00:11:37.920
know python style dictionary or map that

00:11:34.720 --> 00:11:41.079
has it's the key each uh word we would

00:11:37.920 --> 00:11:45.000
look like to look up and is the vector

00:11:41.079 --> 00:11:48.480
the corresponding um index of that

00:11:45.000 --> 00:11:50.480
document so for example what in our case

00:11:48.480 --> 00:11:54.200
here only appears in document one so it

00:11:50.480 --> 00:11:56.279
would point to document one candy uh

00:11:54.200 --> 00:11:58.560
also appears in document one NLP appears

00:11:56.279 --> 00:11:59.839
in two and three and so you can create

00:11:58.560 --> 00:12:02.760
this index IND like this and this is

00:11:59.839 --> 00:12:02.760
called an inverted

00:12:03.079 --> 00:12:08.760
Index this is an important application

00:12:06.000 --> 00:12:11.600
of course so there's lots of software

00:12:08.760 --> 00:12:14.920
the most kind of pical software for this

00:12:11.600 --> 00:12:18.760
is Apache Lucine so if you want to build

00:12:14.920 --> 00:12:21.639
a big index uh to look up vectors using

00:12:18.760 --> 00:12:24.160
this sparse index like this you can uh

00:12:21.639 --> 00:12:24.160
take a look at

00:12:26.160 --> 00:12:30.880
Lucy so the next thing I'd like to talk

00:12:28.399 --> 00:12:33.199
about is dense retrieval and the way

00:12:30.880 --> 00:12:36.000
dense retrieval works is you encode the

00:12:33.199 --> 00:12:37.240
document in query into a dense factor

00:12:36.000 --> 00:12:40.240
and find the nearest

00:12:37.240 --> 00:12:42.160
neighbor in order to do this encoding

00:12:40.240 --> 00:12:44.639
you can use a number of things you can

00:12:42.160 --> 00:12:47.440
use out of the box embeddings or you can

00:12:44.639 --> 00:12:49.959
use learned embeddings specifically

00:12:47.440 --> 00:12:53.519
created for the purpose of

00:12:49.959 --> 00:12:56.240
retrieving and so what we do is we take

00:12:53.519 --> 00:12:57.920
all of these uh documents here we

00:12:56.240 --> 00:12:59.920
convert them into embeddings using

00:12:57.920 --> 00:13:04.040
whatever embedding method that we want

00:12:59.920 --> 00:13:05.920
to use we then have a query and we take

00:13:04.040 --> 00:13:07.720
that query and we match it and find the

00:13:05.920 --> 00:13:10.040
nearest neighbor

00:13:07.720 --> 00:13:13.120
here so if you're just using out of the

00:13:10.040 --> 00:13:14.839
box embeddings you don't need to um you

00:13:13.120 --> 00:13:15.880
know do anything special for retrieval

00:13:14.839 --> 00:13:18.440
you can just take your favorite

00:13:15.880 --> 00:13:22.800
embeddings like the sentence BT

00:13:18.440 --> 00:13:25.639
embeddings or the open AI uh Adda

00:13:22.800 --> 00:13:27.240
embeddings or something like this but

00:13:25.639 --> 00:13:29.519
actually the type of embeddings you need

00:13:27.240 --> 00:13:32.040
for retrieval are kind of

00:13:29.519 --> 00:13:33.519
very special and because of that it's

00:13:32.040 --> 00:13:36.160
important

00:13:33.519 --> 00:13:38.600
to if you're very serious about doing a

00:13:36.160 --> 00:13:39.800
good job of retal it's important to use

00:13:38.600 --> 00:13:41.360
embeddings that were specifically

00:13:39.800 --> 00:13:45.040
tailored for

00:13:41.360 --> 00:13:47.680
retrieval and the reason why it is

00:13:45.040 --> 00:13:50.079
important to do this is severalfold but

00:13:47.680 --> 00:13:53.800
the most intuitive way to think about it

00:13:50.079 --> 00:13:57.600
is if we think about uh the things that

00:13:53.800 --> 00:13:59.440
tfidf does tfidf is giving a very high

00:13:57.600 --> 00:14:03.000
weight to

00:13:59.440 --> 00:14:04.959
contentful words and rare words and

00:14:03.000 --> 00:14:06.639
we're not guaranteed that any random

00:14:04.959 --> 00:14:10.600
embedding that we get is going to do

00:14:06.639 --> 00:14:13.800
that so for example if we just take the

00:14:10.600 --> 00:14:16.160
average word embeddings of every word in

00:14:13.800 --> 00:14:20.160
a sequence it's going to give the same

00:14:16.160 --> 00:14:22.320
weight to all of the words um in the

00:14:20.160 --> 00:14:24.680
output and in fact common words tend to

00:14:22.320 --> 00:14:27.959
have slightly higher Norms than

00:14:24.680 --> 00:14:29.639
infrequent words and so that would

00:14:27.959 --> 00:14:31.880
actually upli common wordss which is

00:14:29.639 --> 00:14:34.639
kind of exactly the opposite thing we

00:14:31.880 --> 00:14:36.480
want so how do we learn retrieval

00:14:34.639 --> 00:14:39.160
oriented

00:14:36.480 --> 00:14:40.920
embeddings the normal way we do this is

00:14:39.160 --> 00:14:43.399
we select positive and negative

00:14:40.920 --> 00:14:46.839
documents and then train using a

00:14:43.399 --> 00:14:50.240
contrastive loss and so an example of

00:14:46.839 --> 00:14:52.519
this is we have a query and then we have

00:14:50.240 --> 00:14:55.519
negative documents for that query and we

00:14:52.519 --> 00:14:58.199
have positive documents for that query

00:14:55.519 --> 00:15:00.079
and uh we form formulate a hinge loss or

00:14:58.199 --> 00:15:04.000
maybe some sort of probabilistic loss

00:15:00.079 --> 00:15:06.560
similar to the Hench loss and uh do fine

00:15:04.000 --> 00:15:06.560
tuning of the

00:15:07.160 --> 00:15:13.440
embeddings so if

00:15:09.399 --> 00:15:16.320
you have gold standard positive

00:15:13.440 --> 00:15:18.800
documents then this is relatively easy

00:15:16.320 --> 00:15:21.040
to train uh because you just need the

00:15:18.800 --> 00:15:23.800
positive documents and then you can get

00:15:21.040 --> 00:15:25.959
Negative documents in a number of ways

00:15:23.800 --> 00:15:29.279
one common way of getting negative

00:15:25.959 --> 00:15:32.279
documents is you just form a batch of

00:15:29.279 --> 00:15:34.560
data and given that batch of data you

00:15:32.279 --> 00:15:37.480
take all of the other documents in the

00:15:34.560 --> 00:15:39.480
batch um all of the documents in the

00:15:37.480 --> 00:15:42.839
batch that are positive for some other

00:15:39.480 --> 00:15:46.399
query and you use those as negative

00:15:42.839 --> 00:15:49.000
documents so you sample 32 query

00:15:46.399 --> 00:15:50.759
document pairs you use the aligned ones

00:15:49.000 --> 00:15:53.759
as positive documents and then use the

00:15:50.759 --> 00:15:57.440
31 other ones as negative documents and

00:15:53.759 --> 00:16:00.279
this is both effective and efficient

00:15:57.440 --> 00:16:02.000
because you can kind of learned from the

00:16:00.279 --> 00:16:05.079
query document pairs all at the same

00:16:02.000 --> 00:16:05.079
time in an efficient

00:16:05.680 --> 00:16:13.680
implementation however this is not

00:16:09.160 --> 00:16:16.279
enough in many cases because that will

00:16:13.680 --> 00:16:19.040
end up having lots of very kind of

00:16:16.279 --> 00:16:20.440
obviously wrong documents because you

00:16:19.040 --> 00:16:23.120
know

00:16:20.440 --> 00:16:25.360
they're documents that are relevant for

00:16:23.120 --> 00:16:27.880
a completely different query and it's

00:16:25.360 --> 00:16:29.880
kind of easy to distinguish uh between

00:16:27.880 --> 00:16:32.319
those you can just at superficial word

00:16:29.880 --> 00:16:34.519
overlap so another common thing to do

00:16:32.319 --> 00:16:35.759
when you're training these models is to

00:16:34.519 --> 00:16:38.160
get hard

00:16:35.759 --> 00:16:40.680
negatives so hard negatives are

00:16:38.160 --> 00:16:44.360
basically negative examples that look

00:16:40.680 --> 00:16:49.399
plausible but are actually wrong and

00:16:44.360 --> 00:16:53.199
so here uh this famous method called DPR

00:16:49.399 --> 00:16:55.880
is it basically learns the uh encoders

00:16:53.199 --> 00:16:57.759
based on both inbatch negatives like I

00:16:55.880 --> 00:17:00.160
mentioned before and hard negatives that

00:16:57.759 --> 00:17:01.360
were created by looking up documents

00:17:00.160 --> 00:17:03.839
with

00:17:01.360 --> 00:17:06.039
bm25 and so the ones that were looked up

00:17:03.839 --> 00:17:07.640
by bm25 you know kind of look very

00:17:06.039 --> 00:17:10.039
similar superficially but they might

00:17:07.640 --> 00:17:12.400
have you know subtle errors in them for

00:17:10.039 --> 00:17:12.400
why they're

00:17:12.799 --> 00:17:17.160
inappropriate there's also methods to

00:17:15.679 --> 00:17:20.000
learn these

00:17:17.160 --> 00:17:23.199
retrievers based on kind of not

00:17:20.000 --> 00:17:26.199
supervised data so one major bottleneck

00:17:23.199 --> 00:17:29.000
if you're taking the positive documents

00:17:26.199 --> 00:17:30.440
from Human annotations of whether

00:17:29.000 --> 00:17:33.440
something is correct or not or human

00:17:30.440 --> 00:17:37.880
clickthrough logs or other things like

00:17:33.440 --> 00:17:40.640
this is that you need that data in order

00:17:37.880 --> 00:17:44.440
to start training a bottle so uh

00:17:40.640 --> 00:17:47.880
contriver is another method that uses

00:17:44.440 --> 00:17:51.520
two random spans within a document is a

00:17:47.880 --> 00:17:54.440
positive pair and random spans from

00:17:51.520 --> 00:17:56.559
across documents is negative Pairs and

00:17:54.440 --> 00:17:58.960
so this can be used for you know very

00:17:56.559 --> 00:18:00.039
very large scale initial pre-training of

00:17:58.960 --> 00:18:02.280
the

00:18:00.039 --> 00:18:04.520
models and then after you've done that

00:18:02.280 --> 00:18:06.840
large scale initial pre-training you can

00:18:04.520 --> 00:18:10.799
then go in and fine-tune it on you know

00:18:06.840 --> 00:18:10.799
actually annotate the data to improve it

00:18:12.120 --> 00:18:18.799
further Okay so we've talked about

00:18:15.159 --> 00:18:21.559
training uh these dense product uh

00:18:18.799 --> 00:18:24.559
models these uh models that look at

00:18:21.559 --> 00:18:27.720
dense embedding overlap for nearest

00:18:24.559 --> 00:18:28.919
neighbors but the problem is in order to

00:18:27.720 --> 00:18:30.919
calculate this you would need to

00:18:28.919 --> 00:18:35.159
calculate it over a very very large

00:18:30.919 --> 00:18:37.960
document base and just taking a product

00:18:35.159 --> 00:18:40.480
between the query and all of the other

00:18:37.960 --> 00:18:42.400
documents in the document base is

00:18:40.480 --> 00:18:46.080
extremely

00:18:42.400 --> 00:18:48.080
costly and so in order to fix this there

00:18:46.080 --> 00:18:49.080
are methods for approximate nearest

00:18:48.080 --> 00:18:52.280
neighbor

00:18:49.080 --> 00:18:54.200
search and these are methods that allow

00:18:52.280 --> 00:18:57.360
you to retrieve embeddings that have the

00:18:54.200 --> 00:19:00.280
maximum inner product between them in

00:18:57.360 --> 00:19:02.520
sublinear time and because you're doing

00:19:00.280 --> 00:19:03.960
the maximum inner product this is also

00:19:02.520 --> 00:19:06.600
often called maximum inner product

00:19:03.960 --> 00:19:06.600
search or

00:19:06.679 --> 00:19:12.360
myips so I'm going to introduce on a

00:19:09.440 --> 00:19:15.360
very high level two common methods to do

00:19:12.360 --> 00:19:19.320
this the first one is locality sensitive

00:19:15.360 --> 00:19:22.440
hashen um or this can also be called

00:19:19.320 --> 00:19:24.799
kind of inverted index as well and what

00:19:22.440 --> 00:19:26.840
you do is you make partitions in

00:19:24.799 --> 00:19:29.320
continuous space and then you use it

00:19:26.840 --> 00:19:31.240
like an inverted index

00:19:29.320 --> 00:19:33.679
so let's say we have a whole bunch of

00:19:31.240 --> 00:19:34.919
embeddings uh I demonstrated two

00:19:33.679 --> 00:19:36.640
dimensional embeddings here but in

00:19:34.919 --> 00:19:38.440
reality this would be you know as large

00:19:36.640 --> 00:19:41.159
as your word

00:19:38.440 --> 00:19:42.880
embedding your query and document

00:19:41.159 --> 00:19:47.120
embedding space so this would be you

00:19:42.880 --> 00:19:49.760
know 512 or 1024 or something like that

00:19:47.120 --> 00:19:53.480
and what you do is you define a whole

00:19:49.760 --> 00:19:56.720
bunch of planes that separate these

00:19:53.480 --> 00:19:59.320
points into two spaces so if this is our

00:19:56.720 --> 00:20:02.520
first plane all the points above the

00:19:59.320 --> 00:20:04.280
plane will get a one for this partition

00:20:02.520 --> 00:20:06.799
and all the points below the plane will

00:20:04.280 --> 00:20:08.840
get a zero for this partition and we do

00:20:06.799 --> 00:20:12.400
it similarly we we create a whole bunch

00:20:08.840 --> 00:20:15.840
of them and then based on this we can

00:20:12.400 --> 00:20:18.440
now assign sparse vectors depending on

00:20:15.840 --> 00:20:21.520
each of these planes so we have uh for

00:20:18.440 --> 00:20:24.000
example the top one uh one0 0 because

00:20:21.520 --> 00:20:26.400
it's on the right side of the blue plane

00:20:24.000 --> 00:20:28.760
and the um wrong side of the red and the

00:20:26.400 --> 00:20:30.679
green planes and then for the top right

00:20:28.760 --> 00:20:32.799
we have one1 because it's on the right

00:20:30.679 --> 00:20:37.159
side of the blueing the green planes and

00:20:32.799 --> 00:20:39.440
the wrong side of the red plane and So

00:20:37.159 --> 00:20:41.000
based on this now we have a sparse

00:20:39.440 --> 00:20:42.600
vector and we already know what to do

00:20:41.000 --> 00:20:44.640
with a sparse Vector right we look it up

00:20:42.600 --> 00:20:49.039
in an inverted index just like we did

00:20:44.640 --> 00:20:51.520
for a sparse um you know sparse lookup

00:20:49.039 --> 00:20:54.520
table so that's one

00:20:51.520 --> 00:20:57.799
method another method uses a graph-based

00:20:54.520 --> 00:21:01.320
search and the basic idea behind this is

00:20:57.799 --> 00:21:02.480
that we create hubs uh and these hubs

00:21:01.320 --> 00:21:05.200
are kind

00:21:02.480 --> 00:21:07.960
of a small number of points that are

00:21:05.200 --> 00:21:09.440
close to other points in the space and

00:21:07.960 --> 00:21:10.880
so we create some hubs and then we

00:21:09.440 --> 00:21:12.200
search from there so if we have a

00:21:10.880 --> 00:21:16.880
similar

00:21:12.200 --> 00:21:19.159
looking uh set of points in the space we

00:21:16.880 --> 00:21:21.520
find these hubs which are something like

00:21:19.159 --> 00:21:24.880
cluster centroids and then based on the

00:21:21.520 --> 00:21:28.559
cluster centroids we then rule down or

00:21:24.880 --> 00:21:31.200
we greatly reduce the number of

00:21:28.559 --> 00:21:33.400
points that we need to be looking at and

00:21:31.200 --> 00:21:36.960
then we search through only those points

00:21:33.400 --> 00:21:38.600
in a more kind of extensive Manner and

00:21:36.960 --> 00:21:41.840
you can even turn this into a tree where

00:21:38.600 --> 00:21:43.760
you have hubs and then you have uh kind

00:21:41.840 --> 00:21:46.600
of mini hubs and then you have all the

00:21:43.760 --> 00:21:50.200
points so this allows you to do a kind

00:21:46.600 --> 00:21:50.200
of tree based or graph based

00:21:50.600 --> 00:21:55.840
search so obviously unless you're really

00:21:54.159 --> 00:21:57.039
excited about these algorithms this is

00:21:55.840 --> 00:22:00.080
something that you probably don't want

00:21:57.039 --> 00:22:01.440
to be implementing yourself um and the

00:22:00.080 --> 00:22:03.000
good news is there's lots of very good

00:22:01.440 --> 00:22:04.480
libraries that help you do this in fact

00:22:03.000 --> 00:22:08.799
there are so many libraries it's hard to

00:22:04.480 --> 00:22:11.960
manage them but some libraries that

00:22:08.799 --> 00:22:13.799
people very commonly use I I think face

00:22:11.960 --> 00:22:17.320
uh FIS

00:22:13.799 --> 00:22:20.200
SS is a widely used one created by uh

00:22:17.320 --> 00:22:23.760
fair and meta and chroma DB is a

00:22:20.200 --> 00:22:27.720
separate one uh that is kind of an AI

00:22:23.760 --> 00:22:30.720
native uh embedding search database so

00:22:27.720 --> 00:22:30.720
both those are good

00:22:32.960 --> 00:22:41.120
options even with intelligent training

00:22:37.880 --> 00:22:42.640
of dense embeddings however there still

00:22:41.120 --> 00:22:45.600
are

00:22:42.640 --> 00:22:48.240
problems and the biggest

00:22:45.600 --> 00:22:51.720
problem that you face when you're

00:22:48.240 --> 00:22:54.000
looking at something like uh cross

00:22:51.720 --> 00:22:56.880
encoders um that sorry when you're

00:22:54.000 --> 00:23:00.240
looking at dense embeddings is that in

00:22:56.880 --> 00:23:02.159
order to form a good dense embedding you

00:23:00.240 --> 00:23:03.840
need to kind of know in advance what

00:23:02.159 --> 00:23:05.799
you're looking for right because you're

00:23:03.840 --> 00:23:09.120
taking a long document you're condensing

00:23:05.799 --> 00:23:10.679
it down into a single embedding and or a

00:23:09.120 --> 00:23:13.320
long passage and you're condensing it

00:23:10.679 --> 00:23:16.200
down to a single embedding and so if

00:23:13.320 --> 00:23:19.520
that during that condensation process

00:23:16.200 --> 00:23:21.240
actually there's other information that

00:23:19.520 --> 00:23:23.159
is relevant to a query but you have to

00:23:21.240 --> 00:23:27.600
throw out because of the limited

00:23:23.159 --> 00:23:30.600
embedding capacity this causes you to

00:23:27.600 --> 00:23:32.320
you know essentially fail at um doing

00:23:30.600 --> 00:23:34.840
retrieval

00:23:32.320 --> 00:23:38.159
appropriately so there's a couple

00:23:34.840 --> 00:23:40.880
methods that can be used to fix this so

00:23:38.159 --> 00:23:42.279
the first method is in contrast to the

00:23:40.880 --> 00:23:44.159
buy encoder which is what I've been

00:23:42.279 --> 00:23:47.000
talking out about at this point where

00:23:44.159 --> 00:23:48.520
you kind of do full encoding of queries

00:23:47.000 --> 00:23:52.120
full encoding of documents and then do

00:23:48.520 --> 00:23:53.840
inner product search for a score uh you

00:23:52.120 --> 00:23:56.760
can use a cross encoder and the way the

00:23:53.840 --> 00:23:58.559
cross- encoder works is you append the

00:23:56.760 --> 00:24:00.799
query and document and then you run them

00:23:58.559 --> 00:24:03.400
through a model like a Transformer model

00:24:00.799 --> 00:24:07.840
and you calculate the output

00:24:03.400 --> 00:24:09.880
score so the problem with this um so

00:24:07.840 --> 00:24:12.480
this this is great uh because it gives

00:24:09.880 --> 00:24:15.799
you maximum flexibility um Transformer

00:24:12.480 --> 00:24:18.799
models are powerful you can uh assess

00:24:15.799 --> 00:24:20.520
relevance very well the problem with

00:24:18.799 --> 00:24:22.200
this is this precludes approximate

00:24:20.520 --> 00:24:23.720
nearest neighbor lookup because now

00:24:22.200 --> 00:24:25.799
you're running through you know many

00:24:23.720 --> 00:24:28.880
many nonlinearities

00:24:25.799 --> 00:24:32.760
here so this is can only be used for

00:24:28.880 --> 00:24:34.360
reranking documents um or if even if

00:24:32.760 --> 00:24:36.880
you're doing retrieval doing retrieval

00:24:34.360 --> 00:24:39.679
over a very very small number of

00:24:36.880 --> 00:24:41.960
documents but if you really want maximal

00:24:39.679 --> 00:24:44.080
accuracy I definitely would recommend uh

00:24:41.960 --> 00:24:45.720
doing something like this because it can

00:24:44.080 --> 00:24:47.960
allow you to do kind of a second pass

00:24:45.720 --> 00:24:49.360
filtering over the most relevant looking

00:24:47.960 --> 00:24:52.399
documents to identify the ones you

00:24:49.360 --> 00:24:52.399
really want to add to your

00:24:54.240 --> 00:24:58.240
context so then there are also

00:24:56.760 --> 00:25:01.360
approaches that are kind kind of in the

00:24:58.240 --> 00:25:02.159
middle of these two uh the most famous

00:25:01.360 --> 00:25:05.880
one is

00:25:02.159 --> 00:25:08.320
Kar and the I called this token level

00:25:05.880 --> 00:25:10.840
dense retrieval it's also called uh late

00:25:08.320 --> 00:25:12.720
interaction in the coold bear paper but

00:25:10.840 --> 00:25:14.919
the way it works is you use

00:25:12.720 --> 00:25:18.440
contextualized representations of all

00:25:14.919 --> 00:25:19.440
query and document tokens to compute a

00:25:18.440 --> 00:25:23.559
retrieval

00:25:19.440 --> 00:25:26.919
score and so you do offline indexing of

00:25:23.559 --> 00:25:29.159
every token in the document and then

00:25:26.919 --> 00:25:31.399
based on this offline X indexing of

00:25:29.159 --> 00:25:35.320
every token in the document you then

00:25:31.399 --> 00:25:38.760
have a query encoder and you do matching

00:25:35.320 --> 00:25:41.799
between each token in the query and the

00:25:38.760 --> 00:25:43.399
highest scoring tokens in each

00:25:41.799 --> 00:25:46.320
document

00:25:43.399 --> 00:25:48.399
and the reason why this is good is it

00:25:46.320 --> 00:25:49.600
still allows you to encode all of the

00:25:48.399 --> 00:25:52.120
tokens in the

00:25:49.600 --> 00:25:55.440
document and but each of these

00:25:52.120 --> 00:25:59.679
similarity searches is still just

00:25:55.440 --> 00:26:03.559
a kind of maximum product search and

00:25:59.679 --> 00:26:06.279
because of this this allows you to do

00:26:03.559 --> 00:26:07.960
each of these searches efficiently and

00:26:06.279 --> 00:26:09.840
doesn't preclude you from running it

00:26:07.960 --> 00:26:12.919
over an entire

00:26:09.840 --> 00:26:16.399
database the downside to this method uh

00:26:12.919 --> 00:26:19.120
may already be obvious but in the

00:26:16.399 --> 00:26:22.200
traditional bu encoder we have a single

00:26:19.120 --> 00:26:26.880
Vector for each document but here we

00:26:22.200 --> 00:26:29.320
have one vector for um each token in the

00:26:26.880 --> 00:26:31.880
document so BAS basically your vector

00:26:29.320 --> 00:26:34.399
database gets n times larger where n is

00:26:31.880 --> 00:26:36.679
the number of tokens in the document and

00:26:34.399 --> 00:26:38.080
there are certain methods to make this

00:26:36.679 --> 00:26:41.559
better like you can compress each

00:26:38.080 --> 00:26:42.960
document to a smaller number of n uh but

00:26:41.559 --> 00:26:45.880
still this is definitely going to be

00:26:42.960 --> 00:26:48.399
more costly than looking up each uh

00:26:45.880 --> 00:26:50.360
token so this is definitely something to

00:26:48.399 --> 00:26:53.520
consider if you want to get you know

00:26:50.360 --> 00:26:55.159
very good scores and Co bear is a good

00:26:53.520 --> 00:26:59.600
implementation of that to start with if

00:26:55.159 --> 00:26:59.600
you're interested in trying it out

00:27:00.480 --> 00:27:07.000
so this is a final thing this is uh

00:27:03.080 --> 00:27:08.679
something that is a little bit uh

00:27:07.000 --> 00:27:10.080
different than all the other things I I

00:27:08.679 --> 00:27:12.399
talked about before but I've used it

00:27:10.080 --> 00:27:15.840
myself and it actually can be pretty

00:27:12.399 --> 00:27:18.799
effective um it was also made at CMU so

00:27:15.840 --> 00:27:24.399
by Lal so I would like to promote our

00:27:18.799 --> 00:27:26.880
CMU work of course but um the HP idea

00:27:24.399 --> 00:27:28.080
between behind a hypothetical document

00:27:26.880 --> 00:27:30.320
embedding

00:27:28.080 --> 00:27:33.440
is that it's actually somewhat difficult

00:27:30.320 --> 00:27:36.200
to match a query and a document right

00:27:33.440 --> 00:27:38.919
because a query is a very short possibly

00:27:36.200 --> 00:27:42.240
ungrammatical output that's asking a

00:27:38.919 --> 00:27:44.799
question and then a document is a very

00:27:42.240 --> 00:27:49.440
long output that's written in a

00:27:44.799 --> 00:27:50.799
different proos style and you you know

00:27:49.440 --> 00:27:53.159
it might have lots of irrelevant

00:27:50.799 --> 00:27:54.519
information or or boiler plate or fluff

00:27:53.159 --> 00:27:57.640
or something like

00:27:54.519 --> 00:28:00.640
that so the idea behind a hypothetical

00:27:57.640 --> 00:28:03.120
document embedding is that it's e easier

00:28:00.640 --> 00:28:05.279
to match a document in a document than

00:28:03.120 --> 00:28:08.159
it is to match a query in a

00:28:05.279 --> 00:28:10.159
document but the input to our model is a

00:28:08.159 --> 00:28:14.360
query right so what do we

00:28:10.159 --> 00:28:17.919
do and so essentially what we do is we

00:28:14.360 --> 00:28:20.399
then take a large language model we feed

00:28:17.919 --> 00:28:23.320
it in a query in a prompt and say

00:28:20.399 --> 00:28:25.399
generate a document that looks like it

00:28:23.320 --> 00:28:30.080
should be the answer to this

00:28:25.399 --> 00:28:32.120
query and so so then the llm goes in and

00:28:30.080 --> 00:28:34.440
it generates a document and hopefully

00:28:32.120 --> 00:28:38.440
this document looks more similar to the

00:28:34.440 --> 00:28:41.440
documents you want to retrieve than the

00:28:38.440 --> 00:28:44.039
um than the original query does and I've

00:28:41.440 --> 00:28:47.240
actually found this to be relatively

00:28:44.039 --> 00:28:51.880
effective at improving accuracy

00:28:47.240 --> 00:28:53.200
on kind of difficult uh tasks especially

00:28:51.880 --> 00:28:55.840
ones that are out of domain from the

00:28:53.200 --> 00:28:58.000
trend models that I'm

00:28:55.840 --> 00:29:01.880
using so I've gone through a whole bunch

00:28:58.000 --> 00:29:04.039
of methods and I would like to finish up

00:29:01.880 --> 00:29:05.679
this section by giving some insight

00:29:04.039 --> 00:29:11.399
about which one you should be

00:29:05.679 --> 00:29:14.559
using so my impression right now is

00:29:11.399 --> 00:29:17.760
that a good basine to start out with is

00:29:14.559 --> 00:29:20.679
something like bm25 it's very easy to

00:29:17.760 --> 00:29:23.080
start out and compared to embedding

00:29:20.679 --> 00:29:26.120
based models it tends to be relatively

00:29:23.080 --> 00:29:28.279
robust to new domains so if you have a

00:29:26.120 --> 00:29:30.559
new domain you're more less guaranteed

00:29:28.279 --> 00:29:32.240
that bm25 will give you some performance

00:29:30.559 --> 00:29:35.320
whereas embeddings may be really good

00:29:32.240 --> 00:29:38.399
but they may be really bad uh depending

00:29:35.320 --> 00:29:40.880
on how out of domain that is compared to

00:29:38.399 --> 00:29:42.799
your underlying embedding

00:29:40.880 --> 00:29:44.760
model

00:29:42.799 --> 00:29:48.039
so however if you want to get the

00:29:44.760 --> 00:29:51.080
highest accuracy definitely tuned models

00:29:48.039 --> 00:29:53.200
are going to be better and if you're not

00:29:51.080 --> 00:29:56.039
worried about computation efficiency

00:29:53.200 --> 00:29:58.480
using something like P bear um with kind

00:29:56.039 --> 00:30:01.320
of the token level retrieval will

00:29:58.480 --> 00:30:05.559
definitely give you uh good accuracy

00:30:01.320 --> 00:30:08.559
here however there's better support for

00:30:05.559 --> 00:30:12.159
bu encoder style models um in kind of

00:30:08.559 --> 00:30:15.240
standard Vector databases like feice and

00:30:12.159 --> 00:30:17.519
uh chroma and other things like that so

00:30:15.240 --> 00:30:19.799
if you want a kind of easier method to

00:30:17.519 --> 00:30:23.279
get started very quickly then using a bu

00:30:19.799 --> 00:30:23.279
encoder is probably the best way to

00:30:25.080 --> 00:30:31.080
go okay so now moving on to actual

00:30:28.279 --> 00:30:33.159
retrieval augmented generation models we

00:30:31.080 --> 00:30:38.360
have uh retriever reader

00:30:33.159 --> 00:30:40.880
models and the way these work is you

00:30:38.360 --> 00:30:43.279
basically the simplest way they can work

00:30:40.880 --> 00:30:45.799
is you basically just chain retrieval

00:30:43.279 --> 00:30:47.640
and reading together so you use an outof

00:30:45.799 --> 00:30:52.519
thebox Retriever and an outof thebox

00:30:47.640 --> 00:30:54.039
reader model and you have your query uh

00:30:52.519 --> 00:30:56.159
you could for example look something up

00:30:54.039 --> 00:30:58.039
on Google get a whole bunch of passages

00:30:56.159 --> 00:30:59.760
and then feed them into a GP key model

00:30:58.039 --> 00:31:03.919
and get an

00:30:59.760 --> 00:31:06.960
answer this overall is quite effective

00:31:03.919 --> 00:31:09.159
um you it's easy to implement and it

00:31:06.960 --> 00:31:10.600
will give you decent results so

00:31:09.159 --> 00:31:15.480
definitely it's something to be worth

00:31:10.600 --> 00:31:20.720
thinking about uh for assignment two in

00:31:15.480 --> 00:31:24.799
the um in the class you're required to

00:31:20.720 --> 00:31:26.679
only use uh kind of public models or

00:31:24.799 --> 00:31:29.760
open source implementations so you could

00:31:26.679 --> 00:31:34.360
still replace that with Apachi Lucine

00:31:29.760 --> 00:31:36.360
and then um you know any standard llm

00:31:34.360 --> 00:31:39.159
and that could be you know llama llama

00:31:36.360 --> 00:31:41.600
Chad or M mistol or mixol or something

00:31:39.159 --> 00:31:45.360
like that so uh you could definitely

00:31:41.600 --> 00:31:48.120
feel feel free to do something like

00:31:45.360 --> 00:31:51.559
that um of course the passages are

00:31:48.120 --> 00:31:53.200
concatenated to the context and so

00:31:51.559 --> 00:31:54.799
because the passages are concatenated to

00:31:53.200 --> 00:31:56.679
context the contacts can get relatively

00:31:54.799 --> 00:31:58.399
long and expensive and other things like

00:31:56.679 --> 00:32:01.960
that but it's just something you have to

00:31:58.399 --> 00:32:01.960
deal with when you're using

00:32:02.600 --> 00:32:07.480
R there are methods also for Retriever

00:32:05.799 --> 00:32:11.600
and Generator endtoend

00:32:07.480 --> 00:32:14.720
training so this is the paper actually

00:32:11.600 --> 00:32:17.600
where the name rag came from and I'll

00:32:14.720 --> 00:32:20.200
use that as an example here uh but

00:32:17.600 --> 00:32:21.600
basically um there are several methods

00:32:20.200 --> 00:32:23.399
that propos to train the Retriever and

00:32:21.600 --> 00:32:27.440
reader to improve

00:32:23.399 --> 00:32:31.240
accuracy and specifically the rag p by

00:32:27.440 --> 00:32:33.200
Lewis at all the way it trained the um

00:32:31.240 --> 00:32:35.639
reader was to maximize generation

00:32:33.200 --> 00:32:38.600
likelihood given a single retrieved

00:32:35.639 --> 00:32:40.279
document and for the retriever it

00:32:38.600 --> 00:32:41.880
maximized overall likelihood by

00:32:40.279 --> 00:32:44.480
optimizing the mixture weight over

00:32:41.880 --> 00:32:46.559
documents so here's kind of a a

00:32:44.480 --> 00:32:50.480
schematic uh which is you have your

00:32:46.559 --> 00:32:54.039
query encoder um you run the Retriever

00:32:50.480 --> 00:32:57.760
with uh maximum inner product search it

00:32:54.039 --> 00:33:00.919
gives you several documents and each

00:32:57.760 --> 00:33:05.880
document has a score and then based on

00:33:00.919 --> 00:33:09.399
the documents and the scores you

00:33:05.880 --> 00:33:11.200
generate uh with each document in the

00:33:09.399 --> 00:33:15.360
context and

00:33:11.200 --> 00:33:17.080
then sum together the probabilities

00:33:15.360 --> 00:33:18.639
multiplied by the weights and I have the

00:33:17.080 --> 00:33:20.320
actual equations here because I think

00:33:18.639 --> 00:33:23.039
it'll be a little bit easier to

00:33:20.320 --> 00:33:25.760
understand after looking at the

00:33:23.039 --> 00:33:28.360
equations so generation is a mixture

00:33:25.760 --> 00:33:31.440
model and you pick a document and

00:33:28.360 --> 00:33:36.519
generate from the document this

00:33:31.440 --> 00:33:40.080
p z given X is the probability of

00:33:36.519 --> 00:33:44.679
picking that document given the query X

00:33:40.080 --> 00:33:48.880
and then this P Theta x z and all of the

00:33:44.679 --> 00:33:51.480
previous tokens is basically the uh

00:33:48.880 --> 00:33:54.840
probability of the next token given that

00:33:51.480 --> 00:33:56.559
you have this particular document so you

00:33:54.840 --> 00:34:00.840
can see that this is basically linearly

00:33:56.559 --> 00:34:00.840
interpr ating between the multiple

00:34:01.559 --> 00:34:05.760
documents and if we look this can be

00:34:04.600 --> 00:34:09.039
considered the Retriever and the

00:34:05.760 --> 00:34:09.039
generator the Retriever and the

00:34:10.839 --> 00:34:16.119
reader one really important thing here

00:34:13.639 --> 00:34:17.760
uh that enables endtoend training is

00:34:16.119 --> 00:34:19.639
they have this probability of the

00:34:17.760 --> 00:34:22.919
retriever be based on

00:34:19.639 --> 00:34:25.480
embeddings and so here we have the

00:34:22.919 --> 00:34:29.040
document embedding and the query

00:34:25.480 --> 00:34:31.440
embedding and the probability is

00:34:29.040 --> 00:34:33.320
proportional to the inner product of

00:34:31.440 --> 00:34:36.599
these exponentiated so you're basically

00:34:33.320 --> 00:34:38.839
taking a soft Max over uh the inner

00:34:36.599 --> 00:34:40.599
product between the

00:34:38.839 --> 00:34:44.200
two

00:34:40.599 --> 00:34:47.919
and this adjusts the retriever to give

00:34:44.200 --> 00:34:49.560
higher similarities to helpful

00:34:47.919 --> 00:34:52.560
documents

00:34:49.560 --> 00:34:52.560
and

00:34:54.040 --> 00:35:02.800
so because the prob probability of the

00:34:59.800 --> 00:35:04.839
retriever model here is included in the

00:35:02.800 --> 00:35:07.160
endtoend probability you don't actually

00:35:04.839 --> 00:35:10.680
need any annotations

00:35:07.160 --> 00:35:12.839
about which documents are useful you can

00:35:10.680 --> 00:35:16.680
just train all of this end to end and

00:35:12.839 --> 00:35:19.480
let backrop do its thing to update the

00:35:16.680 --> 00:35:22.640
uh the retriever as

00:35:19.480 --> 00:35:25.000
well one important issue when training

00:35:22.640 --> 00:35:27.480
models like this is that the search

00:35:25.000 --> 00:35:30.400
index will become stale so what do I

00:35:27.480 --> 00:35:34.760
mean by this if we go back to our

00:35:30.400 --> 00:35:34.760
previous uh thing about dense

00:35:35.480 --> 00:35:43.560
models creating this blue search index

00:35:39.800 --> 00:35:45.400
on the right side of the figure here is

00:35:43.560 --> 00:35:48.680
very costly so like let's say you want

00:35:45.400 --> 00:35:50.520
to embed a million documents or a

00:35:48.680 --> 00:35:55.240
billion documents if you're a big search

00:35:50.520 --> 00:35:58.200
engine company so doing this is very

00:35:55.240 --> 00:36:00.599
slow and

00:35:58.200 --> 00:36:01.920
in contrast doing lookup with kind of

00:36:00.599 --> 00:36:04.160
these approximate nearest neighbor

00:36:01.920 --> 00:36:05.440
searches is sublinear time or even you

00:36:04.160 --> 00:36:08.119
know log time so you can do it

00:36:05.440 --> 00:36:12.319
relatively quickly

00:36:08.119 --> 00:36:15.680
so it's fine to do lookup over this big

00:36:12.319 --> 00:36:17.520
index but if you start updating this

00:36:15.680 --> 00:36:19.920
document embedding you need to recreate

00:36:17.520 --> 00:36:23.760
the entire index and that would be you

00:36:19.920 --> 00:36:27.240
know very computationally costly so the

00:36:23.760 --> 00:36:30.119
solution to this proposed in this rag

00:36:27.240 --> 00:36:33.640
paper by Lewis at all is uh we only

00:36:30.119 --> 00:36:35.640
train the query embeddings and we keep

00:36:33.640 --> 00:36:39.640
the document embedding

00:36:35.640 --> 00:36:41.920
swix there's other Alternatives like um

00:36:39.640 --> 00:36:45.000
there was a paper called realm uh from

00:36:41.920 --> 00:36:48.040
early in retrieval base modeling and in

00:36:45.000 --> 00:36:50.040
that in that method they basically had

00:36:48.040 --> 00:36:51.520
an asynchronous process that was going

00:36:50.040 --> 00:36:55.760
through and using the most recent

00:36:51.520 --> 00:36:59.960
document in better to re-update the

00:36:55.760 --> 00:37:03.359
search index during training but that is

00:36:59.960 --> 00:37:05.960
uh you know kind of a very onerous

00:37:03.359 --> 00:37:07.800
process so I think it's quite common to

00:37:05.960 --> 00:37:11.000
use kind of a fixed document embedding

00:37:07.800 --> 00:37:11.000
in update only the

00:37:12.079 --> 00:37:17.720
queries another thing to think about is

00:37:14.359 --> 00:37:21.160
when do we do retrieval um so there's a

00:37:17.720 --> 00:37:23.079
bunch of different methods the rag paper

00:37:21.160 --> 00:37:24.440
that I mentioned before did this only

00:37:23.079 --> 00:37:26.359
once right at the very beginning of

00:37:24.440 --> 00:37:29.400
generation it grabbed a single document

00:37:26.359 --> 00:37:32.560
and generated the entire output this is

00:37:29.400 --> 00:37:34.800
the default method used by most

00:37:32.560 --> 00:37:37.240
systems however there's other options as

00:37:34.800 --> 00:37:39.640
well you can retrieve uh several times

00:37:37.240 --> 00:37:43.040
during generation as

00:37:39.640 --> 00:37:44.480
necessary and the way this works uh we

00:37:43.040 --> 00:37:46.280
can do this either by generating a

00:37:44.480 --> 00:37:48.480
search token uh saying that we should

00:37:46.280 --> 00:37:50.200
start searching or searching when the

00:37:48.480 --> 00:37:52.640
model is

00:37:50.200 --> 00:37:55.920
uncertain and another way is to do this

00:37:52.640 --> 00:37:58.079
every token so we can do this by finding

00:37:55.920 --> 00:37:59.760
similar final embeddings and using this

00:37:58.079 --> 00:38:02.240
to influence the

00:37:59.760 --> 00:38:04.720
probabilities or approximating attention

00:38:02.240 --> 00:38:06.440
with nearest neighbors so I'm going to

00:38:04.720 --> 00:38:08.920
explain about each of these in a bit

00:38:06.440 --> 00:38:12.480
more detail

00:38:08.920 --> 00:38:16.119
in so triggering retrieval with token

00:38:12.480 --> 00:38:19.720
embeddings is um was proposed by Tool

00:38:16.119 --> 00:38:22.119
forer shik all and the way it works is

00:38:19.720 --> 00:38:25.000
you generate tokens that Tri trigger

00:38:22.119 --> 00:38:27.880
retrieval or other tools so in this

00:38:25.000 --> 00:38:30.079
particular method it uh had several

00:38:27.880 --> 00:38:32.000
tools including asking a QA model or

00:38:30.079 --> 00:38:34.800
getting a calculator or having a machine

00:38:32.000 --> 00:38:37.200
translation system but with respect to

00:38:34.800 --> 00:38:40.000
retrieval augmented generation it had

00:38:37.200 --> 00:38:41.560
this essentially Wiki search

00:38:40.000 --> 00:38:43.680
functionality that would look up

00:38:41.560 --> 00:38:46.680
something in Wikipedia and then use that

00:38:43.680 --> 00:38:46.680
to influence the final

00:38:46.760 --> 00:38:52.200
probabilities

00:38:48.800 --> 00:38:55.160
and the way this was trained is training

00:38:52.200 --> 00:38:59.800
was done in an inative manner where it

00:38:55.160 --> 00:38:59.800
basically generated uh kind

00:39:00.000 --> 00:39:05.680
of examples of tools being useful and

00:39:04.359 --> 00:39:09.560
when the

00:39:05.680 --> 00:39:14.160
tools improve the probability of the

00:39:09.560 --> 00:39:16.119
following output then that would be kind

00:39:14.160 --> 00:39:19.560
of treated as a positive example and

00:39:16.119 --> 00:39:21.520
used to further train the model so this

00:39:19.560 --> 00:39:23.400
was really influential and in fact this

00:39:21.520 --> 00:39:27.000
is how things are implemented in chat

00:39:23.400 --> 00:39:29.319
GPT nowadays not only for um doing

00:39:27.000 --> 00:39:33.400
retrieval but also doing other tools

00:39:29.319 --> 00:39:35.200
like um for example uh generating code

00:39:33.400 --> 00:39:37.440
or generating images or other things

00:39:35.200 --> 00:39:37.440
like

00:39:38.200 --> 00:39:45.079
this another option is to trigger

00:39:40.920 --> 00:39:48.240
retrieval uh with uncertainty estimates

00:39:45.079 --> 00:39:52.280
so flare this is a paper by my student

00:39:48.240 --> 00:39:55.160
Jang bang um where we try to generate

00:39:52.280 --> 00:39:58.560
content and then do retrieval if the

00:39:55.160 --> 00:40:01.800
language model certainty is low so

00:39:58.560 --> 00:40:05.599
here's a schematic of how this works but

00:40:01.800 --> 00:40:09.160
basically um if we have

00:40:05.599 --> 00:40:13.440
some uh retrieved documents we can say

00:40:09.160 --> 00:40:16.560
generate a a summary about Joe Biden and

00:40:13.440 --> 00:40:19.560
when it generates a summary maybe for

00:40:16.560 --> 00:40:20.960
the first output um the language model

00:40:19.560 --> 00:40:22.960
has high

00:40:20.960 --> 00:40:24.240
confidence and because the language

00:40:22.960 --> 00:40:25.359
model has high confidence we just

00:40:24.240 --> 00:40:27.520
generate the

00:40:25.359 --> 00:40:29.599
output

00:40:27.520 --> 00:40:31.839
however in the next step if it might

00:40:29.599 --> 00:40:33.599
generate something like saying Joe Biden

00:40:31.839 --> 00:40:35.680
attended the University of Pennsylvania

00:40:33.599 --> 00:40:37.160
where he earned a law degree but the

00:40:35.680 --> 00:40:39.000
model might not be very certain about

00:40:37.160 --> 00:40:41.560
this it might have a low probability of

00:40:39.000 --> 00:40:45.839
certain important entities and So based

00:40:41.560 --> 00:40:48.839
on this uh we then form a a query where

00:40:45.839 --> 00:40:52.119
what we do is essentially we blank out

00:40:48.839 --> 00:40:55.079
the low probability parts of this and we

00:40:52.119 --> 00:40:57.200
do a search and so this is also a little

00:40:55.079 --> 00:41:00.240
bit like the hypothetical

00:40:57.200 --> 00:41:02.520
edings method where we basically create

00:41:00.240 --> 00:41:04.040
a document that we think will look

00:41:02.520 --> 00:41:07.119
similar to the document that we want to

00:41:04.040 --> 00:41:09.480
find we use that to create search

00:41:07.119 --> 00:41:11.359
results and then we generate the output

00:41:09.480 --> 00:41:13.880
and then we continue doing that and

00:41:11.359 --> 00:41:15.960
whenever we have a high confidence

00:41:13.880 --> 00:41:18.800
output like the one here we don't do any

00:41:15.960 --> 00:41:20.040
retrieval we just you know generate uh

00:41:18.800 --> 00:41:21.880
directly from the parameters of the

00:41:20.040 --> 00:41:23.960
model but whenever we have low

00:41:21.880 --> 00:41:27.400
confidence outputs we do the retrieval

00:41:23.960 --> 00:41:30.400
and base the output on this and so I I

00:41:27.400 --> 00:41:33.119
think this is uh you know a nice method

00:41:30.400 --> 00:41:35.000
that could potentially be uh used the

00:41:33.119 --> 00:41:36.920
downside to that is you might sometimes

00:41:35.000 --> 00:41:38.920
need to generate twice because you would

00:41:36.920 --> 00:41:40.480
generate the output once and then find

00:41:38.920 --> 00:41:42.720
the low confidence parts and generate

00:41:40.480 --> 00:41:45.400
again but you know if you really care

00:41:42.720 --> 00:41:47.319
about the uh kind of quality of the

00:41:45.400 --> 00:41:49.640
output this is I think a reasonable

00:41:47.319 --> 00:41:49.640
thing to

00:41:50.160 --> 00:41:54.920
do okay so now moving on to the Token by

00:41:53.000 --> 00:41:59.800
token retrieval

00:41:54.920 --> 00:42:03.560
methods the kind of original or one of

00:41:59.800 --> 00:42:05.200
the methods that popularized this idea

00:42:03.560 --> 00:42:08.720
of token by token retrieval is something

00:42:05.200 --> 00:42:10.760
called K&N LM and the way it works is it

00:42:08.720 --> 00:42:13.839
retrieves similar

00:42:10.760 --> 00:42:16.680
examples and then uses the following

00:42:13.839 --> 00:42:20.880
tokens from these

00:42:16.680 --> 00:42:23.800
examples and this is kind of like a very

00:42:20.880 --> 00:42:25.839
powerful count-based byr model in a way

00:42:23.800 --> 00:42:28.440
so if you remember back to when we were

00:42:25.839 --> 00:42:32.920
talking about count based Pam models

00:42:28.440 --> 00:42:36.440
what we would do is we would take the

00:42:32.920 --> 00:42:39.400
previous token and we would calculate

00:42:36.440 --> 00:42:41.319
the probability of the next token by

00:42:39.400 --> 00:42:43.040
summing up together all of the next

00:42:41.319 --> 00:42:44.800
tokens and dividing by the total number

00:42:43.040 --> 00:42:49.240
of times that previous token

00:42:44.800 --> 00:42:52.720
occurred and so given that background uh

00:42:49.240 --> 00:42:56.760
we can talk about how the KLM

00:42:52.720 --> 00:43:00.319
works so we have the text context X

00:42:56.760 --> 00:43:02.240
and we want to generate a Target output

00:43:00.319 --> 00:43:04.839
separately from this we have all of the

00:43:02.240 --> 00:43:06.440
training contexts so this is all of the

00:43:04.839 --> 00:43:09.920
contexts that appeared in our training

00:43:06.440 --> 00:43:13.520
data and we encode all of these training

00:43:09.920 --> 00:43:15.720
contexts specifically by calculating the

00:43:13.520 --> 00:43:18.559
representation of the final layer or

00:43:15.720 --> 00:43:21.119
near the final layer of the model and so

00:43:18.559 --> 00:43:23.200
we encode that as

00:43:21.119 --> 00:43:25.240
representations separately from that we

00:43:23.200 --> 00:43:27.920
remember the next word that appeared

00:43:25.240 --> 00:43:29.720
after this Contex

00:43:27.920 --> 00:43:32.920
so now we have a data store consisting

00:43:29.720 --> 00:43:35.040
of representations in next words we then

00:43:32.920 --> 00:43:38.440
take the representation of the current

00:43:35.040 --> 00:43:40.880
context and we calculate the distance

00:43:38.440 --> 00:43:43.400
between the current context and all of

00:43:40.880 --> 00:43:47.119
the other similar context in the

00:43:43.400 --> 00:43:49.839
database we take the nearest K so we

00:43:47.119 --> 00:43:52.440
take the top uh K examples here which

00:43:49.839 --> 00:43:55.240
would be Hawaii Illinois and

00:43:52.440 --> 00:43:57.520
Hawaii we then do uh some sort of

00:43:55.240 --> 00:44:01.440
normalization based on the

00:43:57.520 --> 00:44:05.200
distance and this gives us a probability

00:44:01.440 --> 00:44:06.680
distribution over all of the next tokens

00:44:05.200 --> 00:44:10.599
sometimes these tokens are duplicated

00:44:06.680 --> 00:44:13.599
multiple times and so we aggregate all

00:44:10.599 --> 00:44:15.800
of these counts to be Hawaii for example

00:44:13.599 --> 00:44:18.839
0.8 and Illinois

00:44:15.800 --> 00:44:21.839
0.2 and then we interpolate this with

00:44:18.839 --> 00:44:24.040
the probability given by the standard

00:44:21.839 --> 00:44:26.440
language model using an interpolation

00:44:24.040 --> 00:44:28.400
coefficient Lambda and this gives us our

00:44:26.440 --> 00:44:31.000
final

00:44:28.400 --> 00:44:34.559
probability so the nice thing about this

00:44:31.000 --> 00:44:38.000
is this allows us to explicitly ground

00:44:34.559 --> 00:44:42.079
our outputs in individual

00:44:38.000 --> 00:44:45.319
examples uh and it's a pretty effective

00:44:42.079 --> 00:44:48.760
way to improve the probability of models

00:44:45.319 --> 00:44:53.839
improve translation and other stuff like

00:44:48.760 --> 00:44:56.119
this the disadvantage of doing this is

00:44:53.839 --> 00:44:59.319
that it provides it it kind of ADD add

00:44:56.119 --> 00:45:01.800
an extra component of the model it adds

00:44:59.319 --> 00:45:05.440
extra

00:45:01.800 --> 00:45:08.520
um kind of hyperparameters like Lambda

00:45:05.440 --> 00:45:11.680
and things like this so it is a little

00:45:08.520 --> 00:45:16.960
bit finicky and it doesn't work in all

00:45:11.680 --> 00:45:21.440
situations and so another method that we

00:45:16.960 --> 00:45:23.559
uh proposed or by Manda Birch who gave

00:45:21.440 --> 00:45:26.920
the uh previous lecture on generation in

00:45:23.559 --> 00:45:29.240
this class is unlimi forer and basically

00:45:26.920 --> 00:45:32.680
what unlimi forer does is it notes that

00:45:29.240 --> 00:45:36.079
attention itself is an in inner product

00:45:32.680 --> 00:45:40.440
search and it does topk

00:45:36.079 --> 00:45:42.680
attention and the way we do this is we

00:45:40.440 --> 00:45:45.160
first process the input with a sliding

00:45:42.680 --> 00:45:47.480
window and then perform attention using

00:45:45.160 --> 00:45:49.960
a vector index so if we have a really

00:45:47.480 --> 00:45:54.280
long input that we want to encode what

00:45:49.960 --> 00:45:56.559
we do is we first encode chunks so we

00:45:54.280 --> 00:46:01.960
encode for example AB

00:45:56.559 --> 00:46:03.839
then we encode CD and we encode EF we

00:46:01.960 --> 00:46:06.240
concatenate them together into a big

00:46:03.839 --> 00:46:07.800
index of one long input so in a way that

00:46:06.240 --> 00:46:10.920
this is similar to what they did in the

00:46:07.800 --> 00:46:12.720
KLM you know concatenate all of these

00:46:10.920 --> 00:46:16.520
embeddings into a single

00:46:12.720 --> 00:46:18.680
input but the difference is that this is

00:46:16.520 --> 00:46:21.640
done with

00:46:18.680 --> 00:46:24.280
um the values that we are attending to

00:46:21.640 --> 00:46:27.559
as opposed to just the final

00:46:24.280 --> 00:46:30.079
layer and

00:46:27.559 --> 00:46:33.680
the interesting thing about this is now

00:46:30.079 --> 00:46:36.200
we have an index of one long input and

00:46:33.680 --> 00:46:39.800
when we want to do our next version of

00:46:36.200 --> 00:46:42.240
attention we do KNN search from the

00:46:39.800 --> 00:46:44.280
query we take the retrieved hidden

00:46:42.240 --> 00:46:47.880
States and then we just do attention

00:46:44.280 --> 00:46:50.440
over them so the nice thing about this

00:46:47.880 --> 00:46:53.079
is in the extreme case this makes no

00:46:50.440 --> 00:46:55.240
changes to the model what I mean by this

00:46:53.079 --> 00:46:57.520
is let's say our input was small enough

00:46:55.240 --> 00:47:02.240
that we could coded in only a single

00:46:57.520 --> 00:47:06.400
chunk and for KNN search we also did KNN

00:47:02.240 --> 00:47:09.559
search um we did you know exact Canon

00:47:06.400 --> 00:47:12.400
search over all of the embeddings in the

00:47:09.559 --> 00:47:14.680
trunk in that case this would just be

00:47:12.400 --> 00:47:16.520
normal attention it's exactly the same

00:47:14.680 --> 00:47:18.640
as normal

00:47:16.520 --> 00:47:20.160
attention however there are some

00:47:18.640 --> 00:47:21.760
approximations that go into here like

00:47:20.160 --> 00:47:24.000
when we encode chunks they might not be

00:47:21.760 --> 00:47:26.359
exactly the same as if we encoded the

00:47:24.000 --> 00:47:29.839
entire thing together and we're also

00:47:26.359 --> 00:47:33.640
chopping off some of the values with

00:47:29.839 --> 00:47:35.800
very low um kind of inner products and

00:47:33.640 --> 00:47:37.400
so because of this there are some

00:47:35.800 --> 00:47:38.760
approximations being made but in the

00:47:37.400 --> 00:47:40.160
extreme case if we made no

00:47:38.760 --> 00:47:41.880
approximations this would just be

00:47:40.160 --> 00:47:44.359
exactly the same model as we were using

00:47:41.880 --> 00:47:46.160
before so I find this pretty attractive

00:47:44.359 --> 00:47:48.760
and uh you know empirically it gives

00:47:46.160 --> 00:47:51.720
very good results over long

00:47:48.760 --> 00:47:53.440
distances and you know we can always

00:47:51.720 --> 00:47:56.240
make our approximations better and

00:47:53.440 --> 00:47:57.680
improve this model as well so I I think

00:47:56.240 --> 00:48:00.960
this is a attractive method that you

00:47:57.680 --> 00:48:00.960
might be interested in taking a look

00:48:02.240 --> 00:48:06.200
at okay for the final part of this I'd

00:48:04.559 --> 00:48:08.079
like to talk about long context

00:48:06.200 --> 00:48:12.400
Transformers and these are models that

00:48:08.079 --> 00:48:15.119
are explicitly trained in a way that

00:48:12.400 --> 00:48:16.920
allows you to attend to longer contexts

00:48:15.119 --> 00:48:18.839
in an efficient

00:48:16.920 --> 00:48:21.960
manner

00:48:18.839 --> 00:48:23.680
so one way that we can train over longer

00:48:21.960 --> 00:48:25.880
context is just append all of the

00:48:23.680 --> 00:48:28.040
context together and in fact shortly

00:48:25.880 --> 00:48:32.200
after Transformers came out uh this

00:48:28.040 --> 00:48:34.280
paper by VOA at all demonstrated that um

00:48:32.200 --> 00:48:36.160
it doing this can learn you know

00:48:34.280 --> 00:48:38.119
interesting document level phenomena so

00:48:36.160 --> 00:48:40.440
it can identify when

00:48:38.119 --> 00:48:42.480
multiple uh words refer to the same

00:48:40.440 --> 00:48:43.680
thing or co-reference and other things

00:48:42.480 --> 00:48:45.640
like

00:48:43.680 --> 00:48:47.720
this however the problem with

00:48:45.640 --> 00:48:51.119
Transformers is that computation is

00:48:47.720 --> 00:48:52.799
quadratic in the sentence length because

00:48:51.119 --> 00:48:54.599
you're multiplying all of the query

00:48:52.799 --> 00:48:56.799
vectors by all of the key

00:48:54.599 --> 00:48:59.480
vectors

00:48:56.799 --> 00:49:02.799
and that basically causes a big problem

00:48:59.480 --> 00:49:02.799
if your sequences become very

00:49:03.480 --> 00:49:09.760
long so if we go back to what we did in

00:49:07.480 --> 00:49:12.400
rnns uh from the very beginning of the

00:49:09.760 --> 00:49:14.359
class in rnns they don't have this

00:49:12.400 --> 00:49:16.280
problem because computation is linear in

00:49:14.359 --> 00:49:20.440
the length of the sequence you just pass

00:49:16.280 --> 00:49:22.200
along the RNN State and every single

00:49:20.440 --> 00:49:23.839
time you do the same computation over it

00:49:22.200 --> 00:49:26.559
so there's no quadratic term in

00:49:23.839 --> 00:49:32.400
calculating rnns

00:49:26.559 --> 00:49:34.880
another thing is that when doing rnns

00:49:32.400 --> 00:49:37.680
you can actually P State infinitely

00:49:34.880 --> 00:49:39.040
during the forward pass by just

00:49:37.680 --> 00:49:40.240
calculating the hidden State and then

00:49:39.040 --> 00:49:42.119
throwing away the rest of the

00:49:40.240 --> 00:49:43.359
computation graph that was used in

00:49:42.119 --> 00:49:45.160
calculating that hidden State and

00:49:43.359 --> 00:49:48.319
there's no approximation that goes on

00:49:45.160 --> 00:49:49.680
there so unlike on in un liform that I

00:49:48.319 --> 00:49:51.640
was talking about before where we needed

00:49:49.680 --> 00:49:54.119
to make approximations none need to be

00:49:51.640 --> 00:49:56.400
made in this

00:49:54.119 --> 00:50:00.200
case however there is a problem with

00:49:56.400 --> 00:50:02.040
doing back propop uh because in order to

00:50:00.200 --> 00:50:05.839
do back propop normally you maintain the

00:50:02.040 --> 00:50:09.720
entire you know state of the computation

00:50:05.839 --> 00:50:12.400
graph and so there a common method to

00:50:09.720 --> 00:50:15.280
fix this is basically you pass along the

00:50:12.400 --> 00:50:16.920
RNN state from the previous sentence but

00:50:15.280 --> 00:50:19.240
you just don't do backdrop into the

00:50:16.920 --> 00:50:21.200
previous sentence and this is called

00:50:19.240 --> 00:50:24.040
truncated backrop or truncated back

00:50:21.200 --> 00:50:27.280
propagation through time and this allows

00:50:24.040 --> 00:50:30.160
you to essentially train models with

00:50:27.280 --> 00:50:32.319
infinite context um or at least models

00:50:30.160 --> 00:50:33.720
that can pass along context infinitely

00:50:32.319 --> 00:50:36.359
even if you're not back propping into

00:50:33.720 --> 00:50:36.359
they Cod ear

00:50:37.480 --> 00:50:43.520
there so of course a problem with this

00:50:40.720 --> 00:50:45.880
over long contexts is recurrents uh

00:50:43.520 --> 00:50:47.520
recurrent models can be slow due to the

00:50:45.880 --> 00:50:51.400
kind of sequential dependence they're

00:50:47.520 --> 00:50:54.280
not ideal for um you know running on

00:50:51.400 --> 00:50:57.359
gpus or things like that and this is

00:50:54.280 --> 00:51:01.960
improved by recent architectures like

00:50:57.359 --> 00:51:05.359
Mamba and RW KV which are more conducive

00:51:01.960 --> 00:51:07.079
to GPU Based training um while still

00:51:05.359 --> 00:51:08.599
maintaining linear time complexity and

00:51:07.079 --> 00:51:11.480
so I'm looking forward to talking about

00:51:08.599 --> 00:51:11.480
that more in a future

00:51:13.000 --> 00:51:17.559
class so actually if we take this idea

00:51:15.880 --> 00:51:20.440
of truncated back propagation through

00:51:17.559 --> 00:51:22.359
time this can also be applied to

00:51:20.440 --> 00:51:25.440
Transformers and there's a really nice

00:51:22.359 --> 00:51:27.880
paper Transformer XEL also created by

00:51:25.440 --> 00:51:31.119
kungai who was formerly at

00:51:27.880 --> 00:51:33.119
CMU and what this does is this attempts

00:51:31.119 --> 00:51:35.760
to fix vectors from the previous

00:51:33.119 --> 00:51:39.440
sentence so if we have a standard

00:51:35.760 --> 00:51:40.720
Transformer uh in a Transformer XL

00:51:39.440 --> 00:51:44.640
normally what we do in the standard

00:51:40.720 --> 00:51:48.480
Transformer is each Vector attends back

00:51:44.640 --> 00:51:50.920
to all the other vectors in the current

00:51:48.480 --> 00:51:53.839
context what Transformer XEL does

00:51:50.920 --> 00:51:56.359
instead is when you have a new segment

00:51:53.839 --> 00:51:58.960
that you want to do backrop

00:51:56.359 --> 00:52:01.200
into um you have a new segment that you

00:51:58.960 --> 00:52:03.960
want to basically train over you also

00:52:01.200 --> 00:52:06.400
attend to all of the previous tokens in

00:52:03.960 --> 00:52:07.640
the previous segment but you don't do

00:52:06.400 --> 00:52:10.319
back propop into

00:52:07.640 --> 00:52:12.079
them so this is essentially truncated

00:52:10.319 --> 00:52:14.480
backpropagation through time from the

00:52:12.079 --> 00:52:17.760
Transformer

00:52:14.480 --> 00:52:19.520
perspective this is also really nice

00:52:17.760 --> 00:52:21.200
because what it allows you to do is if

00:52:19.520 --> 00:52:25.880
you have a multi-layer

00:52:21.200 --> 00:52:27.720
Transformer it allows you to attend far

00:52:25.880 --> 00:52:30.520
back so if you look at the last layer

00:52:27.720 --> 00:52:33.520
it's attending um to things in the

00:52:30.520 --> 00:52:36.599
previous context window but the second

00:52:33.520 --> 00:52:39.760
to last layer is attending to things in

00:52:36.599 --> 00:52:41.520
the um not just one context window

00:52:39.760 --> 00:52:44.079
before but multiple context windows

00:52:41.520 --> 00:52:45.760
before and actually this allows you to

00:52:44.079 --> 00:52:47.880
very effectively attend a very long

00:52:45.760 --> 00:52:51.720
context because each time kind of the

00:52:47.880 --> 00:52:54.799
context expands in an exponential

00:52:51.720 --> 00:52:56.520
manner so um recently there's a popular

00:52:54.799 --> 00:52:57.799
model called mistol that I'm sure a lot

00:52:56.520 --> 00:52:59.480
of people have heard about and this is

00:52:57.799 --> 00:53:01.920
using sliding window attention which is

00:52:59.480 --> 00:53:04.160
essentially the same mechanism proposed

00:53:01.920 --> 00:53:09.240
by Transformer XEL so this method is

00:53:04.160 --> 00:53:09.240
still uh used in uh very practical

00:53:10.400 --> 00:53:17.359
systems another paper that has been

00:53:13.440 --> 00:53:19.319
pretty influential in this general area

00:53:17.359 --> 00:53:21.079
is something called sparse

00:53:19.319 --> 00:53:23.359
Transformers and the way sparse

00:53:21.079 --> 00:53:25.960
Transformers work is instead of

00:53:23.359 --> 00:53:29.520
attending to every single previous state

00:53:25.960 --> 00:53:32.640
you attend to every n previous

00:53:29.520 --> 00:53:34.599
States and what this allows you to do is

00:53:32.640 --> 00:53:37.119
this allows you to essentially create

00:53:34.599 --> 00:53:40.319
something like the strided uh

00:53:37.119 --> 00:53:42.079
convolutions or um pyramidal recurrent

00:53:40.319 --> 00:53:45.520
neural networks that I talked about

00:53:42.079 --> 00:53:49.760
earlier um so what this looks like

00:53:45.520 --> 00:53:51.079
essentially is you have um this like if

00:53:49.760 --> 00:53:54.880
you have a particular state it might

00:53:51.079 --> 00:53:56.480
attend to all of the previous end tokens

00:53:54.880 --> 00:54:00.240
but then it

00:53:56.480 --> 00:54:04.400
also attends to all of the

00:54:00.240 --> 00:54:06.880
previous um kind of M chunks so you kind

00:54:04.400 --> 00:54:08.920
of have a combination of local and

00:54:06.880 --> 00:54:11.640
Global

00:54:08.920 --> 00:54:14.760
attention or not local and Global but

00:54:11.640 --> 00:54:16.760
local and kind of longer range attention

00:54:14.760 --> 00:54:18.760
and this can be very effective because

00:54:16.760 --> 00:54:22.319
you can attend to you know much longer

00:54:18.760 --> 00:54:24.079
context with a minimal increase in a

00:54:22.319 --> 00:54:26.520
computational

00:54:24.079 --> 00:54:28.720
complexity

00:54:26.520 --> 00:54:31.160
so another method that's a little bit

00:54:28.720 --> 00:54:32.960
like this uh or it's very similar in

00:54:31.160 --> 00:54:34.359
spirit but slightly different in

00:54:32.960 --> 00:54:35.599
implementation is something called the

00:54:34.359 --> 00:54:37.520
compressive

00:54:35.599 --> 00:54:40.400
Transformer and in the compressive

00:54:37.520 --> 00:54:43.000
Transformer you also have this idea of a

00:54:40.400 --> 00:54:44.319
local memory and then a longer term

00:54:43.000 --> 00:54:47.200
compressed

00:54:44.319 --> 00:54:50.799
memory but you have an explicit

00:54:47.200 --> 00:54:54.319
compression step that

00:54:50.799 --> 00:54:58.079
directly essentially generates this uh

00:54:54.319 --> 00:55:00.960
compressed mem M itself and so this is a

00:54:58.079 --> 00:55:04.119
little bit more flexible I guess it

00:55:00.960 --> 00:55:06.280
allows you to take all of the you know

00:55:04.119 --> 00:55:09.000
relevant things from your local memory

00:55:06.280 --> 00:55:12.000
and compress it down so it's another

00:55:09.000 --> 00:55:12.000
method that's worth thinking

00:55:12.760 --> 00:55:18.400
about finally uh there are some very

00:55:15.799 --> 00:55:20.200
interesting methods that do low rank

00:55:18.400 --> 00:55:23.039
approximations for

00:55:20.200 --> 00:55:25.920
Transformers and so calculating the

00:55:23.039 --> 00:55:29.119
attention Matrix is expensive but this

00:55:25.920 --> 00:55:31.640
is a matrix and because it's a matrix we

00:55:29.119 --> 00:55:32.640
can also approximate it with a lower

00:55:31.640 --> 00:55:35.480
rank

00:55:32.640 --> 00:55:38.559
Matrix and there's a couple methods that

00:55:35.480 --> 00:55:40.599
do things uh like this uh the first one

00:55:38.559 --> 00:55:42.680
is something called Blind forer which

00:55:40.599 --> 00:55:44.520
adds low rank linear projections into

00:55:42.680 --> 00:55:47.319
the model at appropriate

00:55:44.520 --> 00:55:50.359
places and um there's another one called

00:55:47.319 --> 00:55:52.200
NR forer which approximates using the ni

00:55:50.359 --> 00:55:54.440
run method which is based on sampling

00:55:52.200 --> 00:55:56.520
Landmark points but basically the

00:55:54.440 --> 00:56:00.319
general IDE aide behind this is normally

00:55:56.520 --> 00:56:03.400
we do this kind of softmax over you know

00:56:00.319 --> 00:56:06.240
a very large attention Vector but

00:56:03.400 --> 00:56:08.440
instead we can approximate the softmax

00:56:06.240 --> 00:56:11.520
by having some low rank vectors kind of

00:56:08.440 --> 00:56:12.799
like what we used in Laura and uh

00:56:11.520 --> 00:56:16.440
nonetheless get a reasonable

00:56:12.799 --> 00:56:16.440
approximation of the softmax used

00:56:17.799 --> 00:56:24.039
inion okay so we're nearing the end of

00:56:21.520 --> 00:56:26.000
what I want to talk about today and

00:56:24.039 --> 00:56:29.720
finally the thing that I'd like to talk

00:56:26.000 --> 00:56:33.240
about is benchmarks for long PEX models

00:56:29.720 --> 00:56:35.000
and there's a few benchmarks one very

00:56:33.240 --> 00:56:37.359
well-known one is something called long

00:56:35.000 --> 00:56:40.599
range Arena this is a composite

00:56:37.359 --> 00:56:43.000
Benchmark containing mostly non NLP

00:56:40.599 --> 00:56:45.280
tasks and it's definitely used for long

00:56:43.000 --> 00:56:46.760
sequence modeling but the results on the

00:56:45.280 --> 00:56:49.400
long range Arena actually tend to

00:56:46.760 --> 00:56:51.599
diverge uh somewhat from the results

00:56:49.400 --> 00:56:54.440
that you get for longdistance language

00:56:51.599 --> 00:56:56.520
modeling so in addition to this another

00:56:54.440 --> 00:56:58.400
benchmark that I uh personally like and

00:56:56.520 --> 00:57:01.960
have used a bit is something called

00:56:58.400 --> 00:57:05.720
Scrolls which uh combines together a

00:57:01.960 --> 00:57:07.960
whole bunch of kind of QA style or

00:57:05.720 --> 00:57:10.920
summarization style tasks that have very

00:57:07.960 --> 00:57:13.280
long contexts including over narratives

00:57:10.920 --> 00:57:15.680
or books or government reports or other

00:57:13.280 --> 00:57:17.280
things like that so you can also take a

00:57:15.680 --> 00:57:20.680
look at this if you're interested in

00:57:17.280 --> 00:57:20.680
kind of benchmarking longer range

00:57:21.839 --> 00:57:28.280
models okay the final thing I'd like to

00:57:24.559 --> 00:57:30.280
talk about is now that we have retriever

00:57:28.280 --> 00:57:31.680
models we have reader models we maybe

00:57:30.280 --> 00:57:34.000
even have reader models that can

00:57:31.680 --> 00:57:35.520
effectively use very long contexts like

00:57:34.000 --> 00:57:37.880
the ones that we retrieve over whole

00:57:35.520 --> 00:57:39.240
documents how do we effectively use them

00:57:37.880 --> 00:57:43.640
in our

00:57:39.240 --> 00:57:46.680
models so there was a very nice paper um

00:57:43.640 --> 00:57:48.880
by Nelson Leo at Stanford that about a

00:57:46.680 --> 00:57:51.160
phenomenon that was kinded lost in the

00:57:48.880 --> 00:57:53.079
middle and basically what it does is it

00:57:51.160 --> 00:57:55.119
demonstrates that many many different

00:57:53.079 --> 00:57:57.720
models including state-of-the-art model

00:57:55.119 --> 00:58:00.799
models pay less attention to things in

00:57:57.720 --> 00:58:03.960
the middle of long context windows and

00:58:00.799 --> 00:58:06.760
so if we have an answer and we put it in

00:58:03.960 --> 00:58:09.200
you know the first position in Doc in

00:58:06.760 --> 00:58:12.280
you know a concatenated context or the

00:58:09.200 --> 00:58:13.799
20th position in a concatenated context

00:58:12.280 --> 00:58:15.240
it tends to attend more to the ones at

00:58:13.799 --> 00:58:18.359
the beginning or the

00:58:15.240 --> 00:58:19.480
end in contrast the ones in the middle

00:58:18.359 --> 00:58:22.760
kind of get

00:58:19.480 --> 00:58:26.680
lost hence the name lost in the middle

00:58:22.760 --> 00:58:29.520
and the problem with this is you know if

00:58:26.680 --> 00:58:32.480
we are doing something like retrieval in

00:58:29.520 --> 00:58:34.160
Reading then that's maybe not such a

00:58:32.480 --> 00:58:35.680
huge problem because we could just put

00:58:34.160 --> 00:58:37.680
you know the highest scoring documents

00:58:35.680 --> 00:58:39.920
at the beginning that might even be more

00:58:37.680 --> 00:58:42.440
effective than uh you know concatenating

00:58:39.920 --> 00:58:44.160
lots of low scoring documents together

00:58:42.440 --> 00:58:45.559
but if we want to read a really long

00:58:44.160 --> 00:58:48.839
document and synthesize something

00:58:45.559 --> 00:58:52.200
without doing kind of another uh scoring

00:58:48.839 --> 00:58:54.200
step uh that can be an issue and also

00:58:52.200 --> 00:58:56.359
you know our retriever is not perfect so

00:58:54.200 --> 00:58:58.799
we would like the model to the reader

00:58:56.359 --> 00:59:00.520
model to do a good job with the outputs

00:58:58.799 --> 00:59:04.839
that it

00:59:00.520 --> 00:59:06.359
has so there are methods uh to ensure

00:59:04.839 --> 00:59:09.440
use of relevant

00:59:06.359 --> 00:59:12.119
context so of course better retrievers

00:59:09.440 --> 00:59:14.880
make more relevant context you can do

00:59:12.119 --> 00:59:16.240
you know reranking or other things like

00:59:14.880 --> 00:59:17.280
that and only include the context that

00:59:16.240 --> 00:59:19.680
looks most

00:59:17.280 --> 00:59:22.880
relevant um or you know refine your

00:59:19.680 --> 00:59:25.200
reader model but there's also methods

00:59:22.880 --> 00:59:28.720
that can decide whether contact should

00:59:25.200 --> 00:59:32.400
be used in the first place so um there

00:59:28.720 --> 00:59:35.440
are methods uh to decide whether to use

00:59:32.400 --> 00:59:37.559
whether to include passages or not and

00:59:35.440 --> 00:59:39.920
also uh recently we proposed a method to

00:59:37.559 --> 00:59:42.640
filter down to parts of retrieve

00:59:39.920 --> 00:59:44.920
passages uh to have only appropriate

00:59:42.640 --> 00:59:47.480
content and this is a model uh that we

00:59:44.920 --> 00:59:49.319
called filco it basically filters the

00:59:47.480 --> 00:59:52.160
context down to the most relevant

00:59:49.319 --> 00:59:53.920
content that we think is appropriate and

00:59:52.160 --> 00:59:56.960
that allows us to get better results

00:59:53.920 --> 00:59:56.960
when it's fed to the

00:59:57.079 --> 01:00:03.640
generator so that's all I have for today

01:00:00.319 --> 01:00:06.200
um thank you for watching the video and

01:00:03.640 --> 01:00:08.599
for people in the class I'll be happy to

01:00:06.200 --> 01:00:13.079
take questions on Piaza or during the

01:00:08.599 --> 01:00:13.079
office hours that I had planned thanks a

01:00:15.319 --> 01:00:18.319
lot