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	WEBVTT

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	so today I'm going to talk about

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	retrieval and retrieval augmented

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	generation so if we look at our standard

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	prompting flow normally what we do is we

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	combine together a prompt template with

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	an input so if we say please answer this

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	question I think Vin Diesel has been a

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	voice actor for several pictors in TV

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	series do you know what their names

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	are we could get a response from a

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	language model but there are several

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	problems with

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	this the first is accuracy issues

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	the models generally have a knowledge

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	cut off so the parameters are usually

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	only updated to a particular time so for

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	example if a new Vin Diesel TV series

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	comes out then the model that was

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	trained up to a certain time Point won't

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	be able to know anything about

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	it there's also issues of private data

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	so data stored in private text or data

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	repositories is not suitable for

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	training for a number of reasons number

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	one it's not available to to particular

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	language model training providers such

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	as you know open AI or Google or anybody

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	else like this the second thing is

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	Access Control issues so even if you're

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	within an organization that has lots of

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	private data and you can train a

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	language model on that certain people in

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	the organization may have access to

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	certain varieties of data and other

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	people may not so it's not just solely

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	an issue of third party providers it's

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	an issue of organization level Access

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	Control in

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	general in addition there are learning

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	failures so even for data that the model

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	was trained on it might not be

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	sufficient to get the right answer and

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	this is particularly the case for very

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	very large uh training data sets and

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	models that are you know modestly sized

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	because the models very often won't be

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	able to learn from a single look at a

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	particular fact or or whatever else like

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	this especially if iter early in

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	training another thing is even if the

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	answer is correct it might not be

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	verifiable so you might want to be very

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	sure that the model is not making any

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	accuracy

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	problems and so in order to do that very

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	often a human will want to go back to

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	the source of the

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	data so to solve this there's a method

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	called retrieval augmented generation

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	which will also be the topic of our

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	second assignment

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	here and the way it works is you

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	retrieve relevant passages

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	efficiently ones that kind of entail the

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	answer to a question and then read the

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	passages to answer the

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	query so we have documents like this we

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	have a query based on the query we form

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	retrieval we get a whole bunch of uh

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	passages we do reading and then we get

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	the

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	answer so this is in fact implemented in

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	many or even most uh language modeling

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	providers including open AI so to give

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	an example I asked the question that I

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	just said about Vin Diesel's voice

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	acting and TV

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	series and Chad GPT gave me an answer

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	and you can see that J gpt's answer

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	includes several places with quotes um

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	they the little blue quotes

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	there and if you click on the quote it

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	tells you where the information Source

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	came from and so this one says behind

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	the voice actors been

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	Diesel and behind the voice actors TV

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	shows Big Mouth V

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	diesel now if we look

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	closer into this answer we'll see that

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	it's not perfect even though it is uh

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	performing retrieval augmented

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	Generations so for example I only asked

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	about TV series but it's giving me lots

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	of things about movies where it says

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	Groot in Guardians of the Galaxy volume

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	3 2023

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	movie and in fact uh Vin Diesel was not

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	even voicing a character named gut here

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	so that's definitely an accuracy

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	mistake and separately there's a place

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	where it says additionally though the

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	website for big mouthless Vin Diesel it

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	appears to be a misunderstanding or err

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	as Nick croll is credited as the voice

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	of Vin Diesel in that show so there

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	actually Nick croll was acting as V

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	diesel but that's um kind of a

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	misunderstanding of the reader model but

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	anyway you can get the general idea here

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	you can also see that it's not perfect

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	even for very strong models like GPD

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	4 so now I'd like to go into the actual

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	methodology that we use for this uh we

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	have retrieval

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	methods and for the retrieval methods we

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	have uh quite a few different options

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	I'm going to go through each one of them

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	at a time so sparse retrieval document

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	level dense retrieval token level DSE

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	retrieval cross- encoder reranking and

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	blackbox retrieval so blackbox retrieval

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	I'm not really going to go into it a

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	whole lot basically this is just asking

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	a blackbox search engine to retrieve uh

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	you know the relevant context and

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	getting the top several results

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	nonetheless this is a pretty you know

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	reasonable method to do it if you want

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	to do search over you know lots of data

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	that exists on the internet already and

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	that in is what chat jpt does it looks

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	up on Bing by generating a query to

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	Bing so anyway let's go into the actual

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	methods that you develop and control

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	yourself so the first one is sparse

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	retrieval and the way this works is you

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	express the query and document as a

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	sparse word frequency Vector usually

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	normalized by length and so if I ask uh

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	query what is NLP we get a vector where

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	each row the vector corresponds to a

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	different

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	token and we asked what is

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	NLP and so uh the places for what NLP

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	and is will all have a non-zero value

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	and everything else will have a zero

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	value and we also normalize by the

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	length of vectors so we get something

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	like

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	333333 then we have a whole bunch of

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	documents so the first document says

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	what is life can is life someone really

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	likes

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	candy we also have another one that says

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	NLP as an acronym for natural language

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	processing so this is a pretty good uh

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	you

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	know answer to our

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	question then we also have I like to do

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	good research on NLP which is you know a

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	nice sentiment but not a very good

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	answer to our question I

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	guess so if we look at the vectors here

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	we have uh what and candy and is have uh

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	a fairly high

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	score and we have here NLP and is have a

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	high score and NLP has a a nonzero

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	score So based on this um we find the

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	document similarity with the highest

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	inner product or cosine similarity in

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	the document

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	collection and so if we take the inner

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	product between these vectors we

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	actually see that the first one got the

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	highest score because of its

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	relatively High values for the words

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	what and

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	is

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	so as you can see common words like what

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	and is can get a high score kind of

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	regardless of whether a document is very

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	relevant and so one way we can fix this

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	is through something called term

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	waiting and the way that term waiting

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	works is in addition to having this

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	Vector that

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	calculates

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	the frequency within a particular

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	document we also have an upweighting

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	term that gives higher weight to low

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	frequency words because low frequency

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	words like NLP tend to be more

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	informative about whether the document

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	is relevant than high frequency words

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	like what it is because these high

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	frequency words like what and is Could

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	Happen kind of regardless of whether

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	the you know document is relevant the

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	particular terms the person is asking

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	about so one well used and easy to

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	understand version of this is uh tfidf

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	or term frequency indument

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	frequency so the way we Define term

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	frequency is exactly what I talked about

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	before so it's basically the frequency

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	of the term uh T in the document d

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	normalized by the total term frequency

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	within the document so that that's what

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	I already showed in the previous

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	slide and then indument frequency is a

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	little bit more involved but basically

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	the way this works is we have log of the

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	total number of documents in the

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	collection divided

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	by the total number of uh times this

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	term appeared in any particular

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	document and so if a term appears many

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	times in any particular document it will

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	have a low IDF score uh one that's close

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	to zero but if it rarely appears it will

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	have a high IDF score so basically this

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	is upweighting our frequent terms and

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	then for

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	tfidf uh we basically multiply these two

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	terms together and we upweight the low

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	frequency

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	words there's another version of this

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	called bm25 that is uh widely used used

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	um this is more involved so I'm not

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	going to go into all of the details but

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	basically if you remember back to the

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	lecture on count-based language models

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	there were a bunch of smoothing

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	techniques for these count-based

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	language models and this uses uh kind of

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	a m multiplicative additive smoothing

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	term to upway things instead of using

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	the term

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	frequency and uh the actual formula is

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	here K and B are kind of

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	hyperparameters and um average DL is

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	average document length the details of

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	this are not really important but

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	basically what you should know is that

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	this is doing some smoothing on the term

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	frequencies and you can look in more

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	detail if you're

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	interested so now that we have this sort

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	of term

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	based uh sparse Vector we would like to

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	use this to look up relevant documents

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	in a collection very quickly because you

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	know we might have a collection that's

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	extremely large like as large as the

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	entire internet like what Google is

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	doing when it searches and so in order

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	to solve this we need a data structure

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	that allows for efficient sparse lookup

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	of

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	vectors and so we have all of these

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	sparse vectors like this

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	and we uh basically turn this into an

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	index where we have something like a you

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	know python style dictionary or map that

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	has it's the key each uh word we would

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	look like to look up and is the vector

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	the corresponding um index of that

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	document so for example what in our case

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	here only appears in document one so it

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	would point to document one candy uh

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	also appears in document one NLP appears

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	in two and three and so you can create

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	this index IND like this and this is

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	called an inverted

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	Index this is an important application

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	of course so there's lots of software

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	the most kind of pical software for this

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	is Apache Lucine so if you want to build

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	a big index uh to look up vectors using

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	this sparse index like this you can uh

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	take a look at

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	Lucy so the next thing I'd like to talk

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	about is dense retrieval and the way

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	dense retrieval works is you encode the

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	document in query into a dense factor

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	and find the nearest

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	neighbor in order to do this encoding

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	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