Stop Chunking Like It's 2022 — Yuval Belfer, AI21 Labs

0:13 Hi everybody. thank you for coming today. welcome to a talk about nothing. Sorry, a talk about retrieval. my name is Yuval. I work at AI21 which is essentially an AI research lab. And today I want to talk to you about something that most people don't want to talk about which is chunking. And I hope to convince you by the end that chunking isn't dead and there is something to do with that. And really if you are at Axe LinkedIn wherever you probably seen that rug is dead right I think people also killed MCP lately and Rag is dead again.

0:58 Long live identic retrieval, identic search and there is come a time where you have to ask yourself how many times can Rag die right and even when someone says well ra isn't dead like Jerry the CEO of Llama index they still have to kill something and apparently this something is chunking like don't invest in it don't do it and this is the reason that People said that chunking is dead because everybody's using agentic search now, right? You have gs, you have ls, you have finds. All of these are great, but these are still not enough if you have a lot of data and you have various amount of queries.

1:46 Just the second. Okay. And I think that the main reason that a lot of people don't like to talk about chunking, it's because it's not the fun part, right? In every rag or files system we have two stages. The first stage is the like the boring one as you may the one you do in the beginning you have a lot of data you have to pre-process it you have to decide on the chunk size and then you have to store everything in a vector DB the other part is the retrieval part essentially the the one that happens per query this is something which is much easier to do right it's much easier to optimize you can use all your queries and then you can play with the max k top k sorry you can play with a hybrid search maybe those kind of things much more fun to do retrieval tuning right h so I will claim that if we have to kill something if something has to be dead then it's probably retrieval tuning and yes agentic search probably killed that and but still agentic search even if we can accept the fact that it killed retrieval tuning it's still not good enough when you have a lot of right scale a lot of data it costs a lot of money I don't think I have to mention that anymore token maxing is like something that everybody's talking about and the thing underneath which is if the data itself is not ordered in a right way in your folders in your directories you still get something which is inefficient so let's try to think of a like a timely example right the FIFA World Cup is now. And let's imagine that we have a data set that contains of all the FIFA World Cup. So every directory is the let's say the 98 one, the 2002 one and so on and so on. But if your query asks how which team won the most World Cups, you can't just go to a folder and ask that. You have to go to every folder, see who won, and then aggregate this together, which is very inefficient.

4:00 The answer by the way is Brazil. I hope at least according to yeah according to the time that this conversation is happening. So retrieval didn't actually die. Okay, we're not killing anything in this lecture. It is got devoted into plumbing. And I think that everybody who worked on any rug system know the feeling. day one or week one or maybe even month one if you're very thorough. You're picking some sort of a chunk size. Let's say 512 and maybe you're probably putting some overlap right 10 20% so on indexing everything and forget all about it. And you can right we talk a lot about the fixed chunking strategies where if your chunk something which is too big right so you get the whole picture which is nice but you're losing a lot of the nuance and all the chunks will not get meaningful embeddings where if you will choose your chunks to be too small you're getting the big picture lost and really it won't be as efficient. So what this tells us is that chunking is essentially a lossy compression. No matter what we're doing, we're losing something. And I will I will claim that there is no right chunk size. And a lot of you who worked on data will say, "No, but we have this corpus. We have this data set and we really used and we optimized our system to work very very well on this data." And we thought so too. We had a lot of experience with it with a lot of different types of agents and systems and workflows that you can really and right you think about benchmarks how easy it is to overfit your model to a benchmark but not with rug it doesn't happen there and you cannot really optimize it per data set and I will claim that it is query dependent and how can I be so sure how can I claim such a thing because we ran experiments and we tested And now I'm going to present it to you. So what we did instead of saying what is the best chunk size per data let's find out let's let's actually take a data set and duplicate this data set several times.

6:19 In this case six times in every duplication in every instance the chunk size is different. So we have a database with a chunk size of 2,00 a database with a chunk size of 1,000 and so on and so on. And we did it with several data sets. So QM sum which is a meeting transcript data set, narrative QA which is question answering on novels and Seinfeld data set which is a trivia about nothing. Not really. It's a trivia trivia questions about the transcripts of Seinfeld. It's a kind of a trolling data set that we built in-house. We also published it if anybody wants the link at the end.

7:00 And we tested on all of them to see what happens. And first of all, we just wanted to see for every data set which chunk size is the best. And what we're seeing here is an example from the Seinfold data set where essentially two queries which are different by nature get different results based on the chunk size. So the first question what is the name for Jerry's favorite church? You can see this is a very focused question very specific question. The answer to it is probably very contained and this is something that a smaller chunk size will do best in. And you can see rank one versus rank below 50.

7:40 between 100 tokens fixed at chunk size to 100 whereas a question like who does Jerry describe as his nemesis and pure evil which I'm not even that big of a Seinfeld fan and I know it's Newman but if you look at the transcript it's not something you can find that easily and you can see that it really changes right if you use small chunk size you will not get the answer and what we did to really after we ran all of these things and we've noticed that we said what if we had an oracle or a genie if you want that can tell us for every query what is the best chunk size to do retrieval for this essentially is the Oracle experiment this is what we wanted to know to see the potential this is not right we already have the answers so we're not actually building a system here we just want to see what is the potential that we have here and what you can see here. Okay, in this graph all the blue, first of all, the yaxis is the recall. Higher is better. The x-axis is the number of retrieved chunks. So, it's recall at K versus K. You can see all the blue lines probably indistinguishable, but each of them is the performance for a fixed chunk size, whereas the orange one is the oracle line. This is for every query, we took the best one out of these. And you can see it happens across several data sets.

9:11 In a lot of them, you can actually see that the blue lines inter intersect with each other. Meaning that indeed for a lot of the data sets, no chunk size actually dominates. And what's more interesting is that there's a lot of potential. The gap which you can see between the orange line and all the blue lines is big. And when I say big, it's something like 20 to 40% just from doing strategy on chunking and very simple strategy may I add. And this is like that this gap this is what the choice of 512 or a thousand or whatever right this number is just arbitrary.

9:53 this is what it costs you. And I think that the problem here is like it's a bit tricky because it's kind of like an information problem that we don't have the information that we need at every stage. And what do I mean by that? If I'm looking at the indexing part where I do have control over the chunk size, I don't know what the queries will be. I can guess, I can maybe estimate, I I can try but I don't know what the queries will be. So I cannot adjust my chunk size accordingly.

10:28 And the retrieval part where I do have my queries, I cannot control the chunk size, right? It's already fixed and I obviously will not do the entire process per query from the beginning. So we looked at prior works such as notably entropic contextual retrieval where they en enrich every chunk and others that essentially try to improve the latent space of every chunk. But this is not the direction that we went.

10:59 All of them just stayed in the model of let's work with a fixed chunk size. Whereas we took a different approach and we said why commit to one where we can commit to several and we call it the multiscale indexing essentially we're just doing what we've seen before. So we're checking the database. We duplicate it and chunk it with several chunk chunk sizes or window sizes and then sorry and then this is what happens at the indexing and then at retrieval time we are querying all of them. So if we had n duplicates of database n window sizes we now have to run six different retrieval calls per query. Sorry, six is n.

11:50 And how do we combine them? We obviously cannot use the oracle, right? The oracle is something that we have just for potential. In real life, we don't know the answer. but what we can do is to find some sort of merging algorithm. Now you would say when we look at it like this, what can be the issue? the fact that we have n ranking but the rankings are for chunks and chunks with different sizes are not really comparable right so instead we opted to do something which is pretty popular these days and a lot of the rug systems actually work like this that instead of just retrieving the chunk when we're getting a chunk we're retrieving the entire document right when context window grows we want to give more and more context and now in this case we have n right n rankings of the same documents because there are not chunks anymore and this we can compare and in this case you can think of retrieval as essentially just voting right so it's not pure ranking we don't have round ranking and then we're doing it rerank we're having n different ranks of the relevant documents and we want to aggregate them all into one that's why we are using something called rf receive reciprocal rank fusion, okay, which is pretty much a simple formula.

13:13 We tried several things. This worked the best. And as you can see, it's not a model. It's not something that you have to do specifically like especially this is just a simple script that takes really no time. And this is how the full how the full system looks like. So we have the indexing end times. Then we query each query from every database and we're using RRF to combine them all and the results you can guess that they're good otherwise I would not standing here and being way too much confident right but you can see we tested across several data sets QM sam narrative QA Seinfeld and also Finance Bench we took all of them and it matches the best or bits the best fixed size.

14:06 Let's see it in a graph. It's a bit hard to see here, so I'll walk it slowly. Every row here is a chunk size. So you can see 50, 100, and so on. The bottom row is our method. This one, the one that you do from all of them and then combine. And the every column is recall at something. So recall at one two three up until 10. What you can see here is that two things right. First of all that across like recall at whatever h our method still wins which you can think is very easy but the fact that you have to combine all of them is not very it's not something which is very trivial and also you can see that the quality actually increases the heat map where you can see it become much greener and again this was just something that I wanted to show in large here you can see all four of the data sets where we do achieve better results results u really quite like 20 30 40% even in a lot of the things h also there are results that I did not show you here which are on MTB you can see in our blog I will put the link later we're getting there also a lot of improvements somewhere between 10 to 40% depending on the data set now I'm I'm not naive I'm not going to claim here that this costs nothing obviously There is a cost, right? No free lunches. Everything has to come with something. And yes, this costs with extra memory. It costs something between two to five to all of one, right? A constant of additional memory where you have to keep all of those all those copies of the database. However, if you think about it latency wise, it doesn't really affect that because you can do all the retrieval partly and also the RRF part doesn't really take a lot of time.

16:10 I will say that this was a very nice research project that we did and we got really really cool results. There are things to do right there are places to improve. There are future work to do. More precisely, we want to understand how many chunk sizes do we want and and which right the fact that we worked with 50, 100, 200 and so on was pretty arbitrary to be honest. So, we do need to figure out how to compute this and how to know how many copies exactly do you need. Also go beyond RF, right? the fact that we're using RF is because it worked the best from the methods that we used, but it doesn't mean that there is no better method.

16:54 And if I need to leave you with something, I would say that agents didn't kill retrieval. Nothing died. Come on. It's just infrastructure. And the part the bad bad part is that it's infrastructure from 2022. And with really simple simple methods, you can take your rag system or anything that has to do with storing data and then retrieve it with 20 to 40% again without any something too sophisticated.

17:28 So if you want to hear more about read more about it, you can read the blog. There is also an example code there and the Seinfeld data set. And that's it. I'm Yuval. Thank you so much. May here.

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