Orchestrate all the Things - Is scaling all you need for AI Large Language Models? Scaling laws and the Inverse Scaling Challenge. Featuring Ian McKenzie, FAR AI Research Scientist

Episode Date: January 27, 2023

The last couple of years have been an AI model arms race. The assumption is that the larger the model the better it will perform. But that may not always be the case. FAR AI Research Scientist Ian McKenzie is a key member of the team organizing the Inverse Scaling Challenge, an initiative set up to investigate scaling laws. We discuss: Large Language Models and how they are trainedThe scaling laws and how they are being revised as research and development progressesThe Inverse Scaling Challenge and its findings Article published on Orchestrate all the Things.

Welcome to the Orchestrate All the Things podcast. I'm George Amadiotis and we'll be connecting the dots together. The last couple of years have been an AI model arms race involving a number of players from industry and research. Google, DeepMind, Meta, Microsoft, in collaboration with both OpenAI and NVIDIA, are the names most people recognize. But we also have the international research corporation Big Science, Baidu and Tsinghua University from China, Yandex from Russia, Aleph Alpha from Germany, AI21 Labs from Israel and Stability AI from the UK to name just some of the key organizations working in the field. All of them are developing different versions of what is called large language models. Large language models are trained on huge corpora of text and feature parameters that are measured in the billions.

These are the types of models used to power headline-grabbing applications like chatGPT, as well as a growing number of applications across different domains, ranging from content marketing to biology and law. As this is an ascent domain which is a mix of engineering, art and science, more findings are gradually unearthed as more effort is put into developing those large language models. Initial empirical findings suggested that there is a correlation between the number of parameters in those models and their performance, something which came to be known as the scaling law. Later findings, however, suggest that this may not always be the case. In fact, sometimes the correlation may be an inverse one. In other words, larger models may actually perform worse than smaller ones for specific tasks.

The inverse scaling challenge is an initiative set up to investigate this hypothesis. In today's episode, we host Ian McKenzie, FAR-AI research scientist, who is a key member of the team organizing the Inverse Scaling Challenge. We discuss large-language models and how they are trained, the scaling laws and how they are being revised as research and development progresses, as well as the Inverse Scaling Challenge organization and its findings. I hope you will enjoy the podcast. If you like my work, you can explore more of it and follow Link Data Orchestration on the web, Twitter, LinkedIn, Facebook, YouTube, as well as all major podcast platforms. So I'm a research scientist at FAR AI, which is a research organization interested in AI alignment.

I kind of focus on the technical AI alignment typically. Before this, I was an intern at another AI alignment related company called OTT and before that I was doing a master's in artificial intelligence. So yeah, I've been kind of moving towards AI stuff for the past past couple of years and in particular the kind of alignment angle. Thank you. And well, what's the focus of today's conversation is an effort you are involved in, along with a few colleagues of yours, I guess, which is called the inverse scaling price. models and how they are trained, some specific parameters around those. So for the benefit of people who may be listening, who may not be necessarily familiar with how all that works, I think it would be beneficial if we start by just saying a few words around large language models, what they are, how they're trained, and well, the role that size, and specifically the size

of the data set that's used to train large-vaguance models, and how it plays into the training process and the results, the quality that you get out of them, and how all of that is related to what you do with the inverse scaling price? Sure, yeah. So large language models, they've been all the rage the last few years. They're extremely large machine learning systems, often with billions of parameters, and they can cost huge amounts to train, that are basically designed to learn how language works and to be able to reproduce language. And typically the way that you train one of these is you take a really large pile of text, like a corpus script from the internet, and then you just train the system to predict the next word.

This is the kind of most common way that this is done. And after it's seen lots and lots and lots of text, it becomes surprisingly good at being able to reproduce coherent text, do basic reasoning tasks, all sorts of things, just from this pretty simple objective of how well can you predict what's going to come next on some random internet document. So they, yeah, as you mentioned, there's this idea of scaling, and we have these empirical results called the scaling laws, which show that as you increase the amount of data that you use to train these models, as you increase the size of the models, like the number of parameters that they have, and as you train the amount of compute used to train them,

the performance goes, it gets predictably better in a really surprisingly predictable way. And this has kind of spurred more interest in continuing to make these bigger and give them more data because we seem to just keep getting more and more performance out of them when we do this. So there was this one set of scaling laws originally. People typically call these the Kaplan scaling laws that were popular around when GPT-3 was released. And then last year, DeepMind kind of updated them. They found this, these are called the Chinchilla scaling laws, where they found that actually you can, the ratio, the details are a bit complicated,

but you can just feed them a lot more data and they'll get even better. The ratio of how big you need the model to be to how much data you give it was different than we were expecting. There's been a lot of interest in scaling up the amount of data that we can feed to these, as well as their overall size. Yeah. Okay. So it seems like, it sounds like the general consensus, let's say, around how these models are trained and how the size of the dataset that is used to train them correlates to their performance is that, well, the more the better, basically. You on the other hand, with what you're involved in seem to be making sort of a counter counterfactual argument that well there may be some

specific scenarios or some specific application areas let's say in which this sort of scaling lower scaling hypothesis doesn't really apply. So would you like to tell us a little bit more about what your hypothesis is and how you're going about trying to investigate it? Of course. Yeah, I should say in general, the scaling laws results are very impressive and I put a lot of weight on them continuing to hold. So overall, I think that these are a good description of how language models, how you could expect the loss of these language models to keep going down as we keep making them bigger. But that being said, yeah, what we're specifically looking at is kind of edge cases and the potential for some failure modes

to kind of slip through. Because, like I said, we typically just train these to predict internet text. And we've been surprisingly lucky, depending on how you look at it, that this has transferred to quite a lot of general ability to do reasoning and to do arithmetic and to do sentiment analysis and various other downstream tasks, even though the focus of the training is just on predict the next word

and to mimic the internet. But you might expect, what our guess is, is that there's some areas in which this isn't as helpful, where there's some things on the internet, like tasks that humans aren't that good at. We sometimes make specific logical fallacies and cognitive biases. There's a lot of misinformation on the internet that gets spread for various reasons. There's lots of discrimination and social biases represented. And as the language models get bigger, they're more likely to pick up on these kinds of things and repeat them in contexts that we don't want necessarily. There's another kind of class of failures is ones related to the inductive biases of the models, which inductive biases are generally the ways in which they're

more likely to learn, the kind of idiosyncrasies of the model that generally make it effective at learning the patterns, but sometimes the patterns can be overrepresented. So some things are like these language models often repeat text that they've seen in the prompt. When you feed them some text, they're likely to kind of repeat it even when it's not necessarily appropriate or to get caught in loops is another similar idea.

And maybe the larger models are more prone to this is one hypothesis. Okay. So, you've put together, well first of all it's important I think to say that you're investigating it with a group of people. Oh, I was just saying that I think it's important to to stress that you are investigating this with a group of people. So I was just wondering if you could say who these people are, what brought you together,

and who is really organizing and funding this inverse scaling prize, because there is a prize actually involved, so people will be winning money by sending you submissions that you're going to be evaluating. So just a little bit around the meta, let's say, and the background of this effort. Sure, yeah. So I first got involved in this, my supervisor, Ethan Perez. He was a PhD student at New York University, and he was looking for research assistants and interns to work on various projects with him. And one of his projects was related to this idea of inverse scaling.

So I've been working with him since around February of last year of 2022. Also working on the project, there are a group of other collaborators, including Sam Bowman from NYU. Ethan and Sam both work at Anthropic now, but this project is not affiliated with Anthropic. It is kind of... Also, I'm technically employed by FarAI, which is, like I mentioned,

a group focused on alignment. Okay. So, would you be able to say just a few words about who is really sponsoring both your effort and, well, you kind of touched upon your effort, I guess, by saying that, well, who employs the people who are involved? So I guess that means that, well, they're paying for your time, basically, which is mostly what's needed. But I guess that, well, besides your time, there's also the actual price that's involved. And I read actually in your website, sort of a fun incident, it seems

that part of the price money that were at stake was coming from FTX. So it seems like at the moment you're out trying to substitute for that part of the stake. So I was just out of curiosity, really, wondering who has such an interest in sponsoring this effort of yours. Yeah. So I don't know if, what should I say? We have not taken any FTX money. We had some other money set aside

from other projects that Eason have been working on that we were able to to use and we have another funder but we at this point until the due diligence has been done can't disclose publicly who they are but the price we have we basically are confident that we have secured funding to cover it. Right so speaking of motivation I was just again curious what's your personal motivation for participating in this what motivates the team that you work with and what do you think actually motivates the people who submit to this competition? I mean, besides the obvious fact that by doing that,

they may actually win at least part of the prize. Yeah. So, like I said, my main motivation is AI alignment. This kind of idea that as we build more and more powerful systems, there may be some kind of misalignment between what they do in practice and what we actually want them to do just as a consequence of our objectives not the things that we train them to do not being exactly what we want it's very hard to specify what we want like for example with large language models we're training them just to predict text but this isn't necessarily what we actually want them to do. We want them to reason well and to give good advice

and to be generally helpful and so on, rather than necessarily reproducing just anything that they see on the internet. So this is the angle that I'm coming at it from. I think this is similar for some of my collaborators. Some find it just an interesting technical problem. Others are similarly interested in alignment. I think in terms of the participants, many are,

well, we kind of presented a reasonably well-scoped problem. Like there's a fairly clear metric of does the line go up or does the line go down, which makes it kind of easy to approach and accessible to people who haven't got necessarily like a super strong technical background, although people with strong technical backgrounds were also able to participate. We kind of provided a decent amount of setup for infrastructure around it to run these experiments. So you basically just had to provide the data set. We also had other people who were interested in getting more involved in alignment. And this was like a nice kind of first step for them

in terms of kind of getting involved with the field and doing research in this area. Okay, I see, interesting. So it sounds like it's the right time to ask you, how did the actual competition work? Because you said you did provide some guidelines and some guidance as well for people as to how toMs? Like, I don't know, compute power to run or some interface through which they could access some pre-installed language models? Yeah, so the way the contest worked was basically we kind of set up some guidelines about what would make a good submission, how many examples to use, the kind of

general theme and format. We provided like three different metrics to score datasets against, and then we produced these Google Colabs, which are like interactive coding kind of tools to run your dataset against these large language models. So some of them are available on Hugging Face, like the OPT models and the GPT-2 models and other in those families. And then OpenAI have an API that you can use to access GPT-3, which is like kind of the largest publicly accessible... OPT is similarly sized, but it's certainly one of the best models

that you can access. And we had some, we supplied some credits for accessing these, I think with the help of OpenAI and then provide an interface to use this. Okay, all right. So you tried to make, well, people's lives easier as much as possible, I guess. And I also had the chance to quickly look at the criteria that you provided for evaluating

the submissions. And I think it would be interesting to just quickly go through them and explain what each criterion is and why you have chosen it. Sure. Yeah, so we have six criteria. They're somewhat overlapping but these capture the general... they capture the spirit of what we're looking for. So there is... one is about the inverse scaling strength, so like how strong of a trend do we see? Like how much worse are the larger models than the smaller models at this task? Another is the generality, the

inverse scaling generality. Like on how many of these model families do we see this kind of inverse scaling trend where the larger models are doing worse than the smaller models? A third is the task importance. Like how relevant do we think this task is to the use of language models in everyday use and kind of industry use? If we saw this failure mode, how big of a deal would that be? Another is the novelty and surprisingness, like how much of a new result is this? How different is this from everything that we've seen before? We have the task coverage, which is how well does the data set that you've submitted represent the idea that you're trying to demonstrate?

Like how complete is your coverage of the task that you're trying to check? So maybe you produced quite a narrow data set that tested one specific angle of the thing that you claim is going on, maybe you could have provided more types of examples that kind of attack the same underlying phenomenon, which would be more convincing. And related to this one is the reproducibility, like if someone else were to try to follow along and make a similar data set to yours, how well would that work? How likely would that be to produce similar results? Or do we think it's quite specific to how you formulated the task? Yeah, I think reproducibility is a really important one. I was actually a little bit surprised to see that as a criterion of its own, because I was kind of assuming that this is not

just a criterion, this is a very strict requirement actually that you should absolutely be able to reproduce whatever result comes out of it. So that makes me wonder whether you actually... I was just saying that in terms of reproducibility I was kind of assuming that this wouldn't just be a criterion, this would be like a very very strict requirement in assuming that this wouldn't just be a criterion. This would be a very, very strict requirement, in fact, that results absolutely need to be reproducible. Yeah, I think this is a good point. I think we're looking at something maybe broader, which is how obviously, if we run the dataset again, if we run our models again on the same dataset,

we will get the same results. So in that sense, it's reproducible. It's how far can we stray from how you've set up the task and still get the same behavior. And there's always degrees to how much this is the case. So in the best case, anything even similar, anything pointed at the same phenomenon shows exactly the same trend. In other cases, maybe it gets weaker if you change too many parameters or too many details of the setup. But yes, in general, it is important for the results to replicate. Okay. And did you also have some kind of weighting scores in how you took your criteria into account or was

just everything like, I don't know, six criteria each one gets its equal weight and you just get results that way? Yeah, we mostly scored the submissions across each axis and then ones that had sufficiently many in the... that passed sufficiently many of the criteria at a high level we would consider. There's some subjectivity and kind of... what's the word? We had to aggregate the views of all the reviewers and the organizers so there was some judgment calls to be made there. But in general, we managed to basically score on each criteria and then choose the ones that scored best overall. Okay. And another thing to note about how this all worked is that you had two rounds of submissions and evaluation. So round one

and then round two which I think the evaluation for which is just about finished or close to finishing at least. So I was wondering if you could just quickly browse us through, well first of all why did you do that in two rounds and then what were the results that came out of round one and round two? I should also note before I let you actually answer that, it looks like at least some of the people who submitted for round two had already submitted for round one. And so you basically gave them the chance to iterate further over their efforts. Yeah, so that was one of the reasons that we wanted to run this in two rounds, is that people

could submit to the first round, they could get feedback, they could get a sense of how the competition worked, and then they could submit again to the second round. And we had quite a few people who tried that and improved their submissions for the second round, which is good to see. Also, running it over two rounds kind of split the work for us. We were able to have more total time and we could review submissions in two batches rather than all together, which made this easier to do logistically. We had about 43 submissions for the first round, and then 48 for the second.

In terms of winners for the first round, we had four that we gave third prizes, which is our lowest prize tier. Do you want me to go through those briefly? Or what should we focus on? Yes, we can go through them. Actually before we do, I was going to ask you actually to quickly go through your winners, let's say. But before we do, I had a kind of more higher level, more abstract question, let's say,

having to do with if you have any insights as to the backgrounds of the people who submitted. So first of all, it sounds like between round one and round two there wasn't that much of a difference in terms of the number of submissions. So I was wondering if you could just, you know, extract some insights, let's say, about, well, first the backgrounds of the people who submitted and second if you did see any kind of difference well there wasn't that much of a quantitative difference but did you notice a qualitative difference between the submissions you got in round one and round two so did it work I mean your plan did people actually learn and did your day submissions get

better over time yeah so we didn't collect any strict demographic data around the participants, but I think my general impression from kind of talking to some of the submitters is that we had a lot of PhD students and other undergrads, people early in their kind of research careers, some more established researchers at small labs and things like that. That was the general type of person who was taking part in our contest. In terms of qualitative differences between the rounds, I think for the second round submissions were on average a lot longer. They had more parts to them.

And I think people did take the feedback that we had in the first round on board and attempted to improve their submissions according to that. So it was good to see the improvement between the rounds. And a couple of them, we liked their improvements enough that we'll update their submissions. I think we won't award them the higher price tier, but I think they're likely to receive a better... We'll update the data set that we got from them, basically. Okay. Well, one thing that obviously stood out for me by reading through your results

was the fact that you didn't actually assign any grant prize. I mean, there were different levels of prizes, as you said earlier, and it seems like none of the submissions actually stood out enough to be assigned a grant prize. You did assign a number of, I think, thirdgrade prizes, which I'm going to ask you to walk us through. But first, let's start with the elephant in the room. So what does the fact that you didn't think there was any submission worth your grand prize actually tell us?

I think it just demonstrates that it's quite a challenging task, really, to demonstrate very convincingly this inverse scaling result. I think there's a number of factors that make it tricky. One is that in order to make the contest accessible we kind of limited the scope of the types of submissions that we could accept and so we kind of focused on these narrow metrics and this was definitely helpful like we could kind of make it easy for people to make interesting tasks that way but being

making really convincing demonstrations this way was was tricky I think especially with the amount of time that people were able to dedicate to this this was like a contest that they were doing on the side typically I imagine rather than a full-time research project. Getting enough evidence and making a compelling enough case that this was like a really strong effect and really worrying was challenging. I think this is, it's just quite noisy as well that the language models with the data sets of roughly the size we're looking for, there's often a lot of noise in the results which makes it hard to be extremely sure of anything. So we had pretty strict criteria for the grand prize.

We wanted to be really convinced of the importance and like surprisingness of the task and I think it was a high bar and unfortunately we didn't have any submissions that passed it but we were still very pleased with submissions that we did get. I think the people really put a lot of effort in. Okay so I guess from your, it sounds like you're not really convinced that this inverse scaling effect is not there. It's probably, it may have to do with, you know, the way the competition was organized or the time that people were able to dedicate to it, rather than actually the results showing that, well, there's nothing to worry about, so to speak. JOHN MUELLER- I think, yeah, I think there is. We definitely had some interesting preliminary

results, I think is one way to put it, and I think which demonstrate that there are effects going on, just nothing crisp and clear enough to pass the strict criteria of the grand prize. Okay, so what are those interesting results that came out and to which you assigned your prizes? Sure, I can go through the prizes of the first round. We had a task called NEQA, which stands for negative QA. This task is about what happens when you invert common knowledge questions and models are not very good at handling negation. When questions are negated, they often answer them as if they weren't. And we see inverse scaling on tasks like this because the smaller

models answer randomly, they're not very good at answering the questions at all, and the larger models start to answer the unnegated question and miss the negation. This is our kind of explanation for why this is happening. Another good one was the quote repetition, which shows that if you present a language model with a series of sentences and ask it to repeat them, and you can show it some examples of a sentence and then it's being repeated, you then show it a famous quote and you change the final word. For example this one is, All the world's a stage, and all the men and women merely players. They have their

exits and their entrances, and one man in his time plays many pango. Which is not a real word. The famous quote ends parts, but because the language model is expecting to see parts it is more likely to choose to choose to assign higher probability to the word parts than to the word pango despite being asked to repeat verbatim and larger models are more likely to do this because I imagine they've just memorized these famous quotes more strongly and so are more likely to kind of repeat them when when asked. The third task that we gave a third prize to is the redefine math task,

which basically asks a language model to take a famous symbol, like the symbol for pi, or one of the, like a multiplication symbol, and to treat it as some other object. So we have one of the examples is redefine pi as 462. And then the question is, what is the first digit of pi? And if the model is able to work with the redefinition in the context, it should say four instead of three. But larger models, again, the one potential explanation is that they are more stuck in their ways in some sense. They're like more strongly memorized context from the internet, and they imagine they're more likely to just repeat, to be stuck on the idea that pi is 3.14 and so on. The final third prize winner from the first round

was called hindsight neglect. And in this one, the context is slightly trickier to set up. Basically, you present a series of bets that the model supposedly has to take where it can either win or lose money and you set up all of the bets such that it's the right idea to take the bet, like it has positive expected value, And then you say that the bet went the way, sorry, it's either the right way to take the bet or the wrong way, the wrong idea to take the bet. And in each case, you make the actual outcome line up with the expected value. So if the expected

value is positive, and it seems like a good idea to take the bet, then it went well. And you ask the model, should it have taken that bet? And you give the example, you give 10 examples of this, where it is a good idea to take the bet and it went well, it was a bad idea to take the bet and it went poorly, and then you show it an example at the end, where it was a good idea to take the bet, but in actuality it turned out badly, and you ask, should it have taken the bet?

And the answer that we expect is yes, because you should take bets that are likely to turn out well. But because the model was paying attention, the hypothesis is the model was paying attention to the wrong feature of the examples, it says no. It's paying attention to the actual outcome rather than the expected outcome, and so falls for this trick. Well, I would argue that many people would actually also fall for it. So it has to do with reasoning. So it may be a little bit too much to ask of a language model. It is a tricky one.

One thing that we did do is pass all of our prompts, we passed samples of the tasks to human contractors to see how they would answer these and we wanted to make sure that the answers that the models are expected to give are answers that humans are able to come up with. But this one, there is some disagreement. This is like a tricky one that some people do fall for as well it is it is an interesting edge case there. All right another comment I have has to do with memorization so in the previous example in the previous prize that you mentioned you gave out well you very aptly described it by saying well it seems like maybe some of these models are kind of stuck in their ways somehow, you know, because the when newer data is presented to the model,

it is actually able to retract part of the data set that it has been trained on? Yeah, this is an interesting question. I don't know what the kind of state of the art is here. It's possible that some of the recent approaches, like reinforcement learning from human feedback, some of the chat GPT type ideas, will make language models better at this kind of task, but I actually don't currently know how to approach this one. I think it's an interesting open question. Yeah, it sounds exactly like it. Okay, so let's then get through the prizes that you gave out in the second round as well.

First, before you start, was there actually any of the prizes of the first round prizes that also got the second round prize? I think there were some that we updated but didn't upgrade their prize, so we thought that they improved but not enough to move up to the next um to the next prize tier um yeah so we have uh once the once this is live we will have uh confirmed um seven uh prizes for the uh the second round um seven third prizes um. I will run through them pretty quickly because there's quite a few. We have one called modus tollens which shows kind of a failure of logical reasoning. Again it has this kind of negation involved. Basically it sets up some logical reasoning

that's supposed to be valid and the language model doesn't realise that it's valid, possibly because of this kind of negation that's going on. We have Into the Unknown, which is a task where we give some information and we ask what other information would be most relevant to help answer a question, and the language models kind of prefer information they already know to information they don't have. This is an interesting task. We have a prompt injection task where the prompt contains instructions to perform one task and to ignore any attempt to change the task that it is doing. And then one of the lower down in the prompt when you're actually asking it a question, it contains what's called like a kind of a malicious query, a malicious question that

is asking it to perform some other task. And we want the language model to avoid falling for this. But language models, the larger they are, the more likely they are to is asking it to perform some other task and we want the language model to avoid falling for this, but language models, the larger they are, the more likely they are to kind of go along with whatever it says later on in the prompt and kind of fall for the prompt injection attack. This was popular, this kind of idea was popular on Twitter about kind of this real estate bot that people were performing this attack on. And so this is an interesting version of that as formulated as an inverse scaling task. We have the repetitive algebra, which is similar to the hindsight neglect one,

where you get the language model to focus on maybe the wrong details of repeated questions where you show a bunch of examples, and then you show an example that is similar in many ways, but maybe slightly different in another, and the language model kind of falls into the wrong pattern. We have a task about significant figures, where language models keep rounding numbers to the wrong numbers of different figures. This is just

an interesting detail where they are calculating them according to... often they round them to the... they don't understand the concept of significant figures and surprisingly they get it more wrong the larger they are. Possibly they're more confident in some other answer. This one is quite interesting. We have one about pattern match suppression, which involves giving a kind of constant pattern and then asking for an example at the end that would break this pattern. But this is kind of related to some stuff I mentioned earlier on. Language models love repeating patterns, and so they find it quite hard to

break out of the pattern at the end. And the larger models are even less likely. They assign more probability to continuing the pattern than to breaking the pattern. Another related task is the memo trap, which is one where you ask, it's also similar to the quote repetition one, you involve, you kind of present some famous quote, and you ask the model to complete it in a way that is different to the famous quote. So for example, write a quote that ends in the word heavy, absence makes the heart grow. And then the model often just repeats fonder the kind of classic answer rather than

using the word that was presented. So these are the main prizes that we'll be awarding in the second round. Okay, so an interesting aspect of the work that you did and that I've had the chance to preview was the fact that you have extracted some insights and some patterns really through all the submissions that you examined and I think that's a very interesting aspect of it and so I would like to ask you to share what you learned, basically. Sure. So we're not done investigating the tasks that we have yet. I think there's more lessons to draw from them. But some of the things that we've found so far

are some of the things I've been mentioning as we go. For example, often in the inverse scaling tasks, we find that the performance on the smaller models starts out about random. The models are kind of, if it's like a classification task, they're kind of randomly selecting between the options because they just really don't know how to solve the task. And then the inverse scaling starts

when the model kind of, quote unquote, thinks that it understands how to do the task, but really is getting it wrong from our perspective and so becomes more confident on the wrong answer. So this is a general pattern that we see. And one way that this can happen is that you have what's quite a hard task and within that is an easier task as a subcomponent. So first it has to solve the easier task and then solve the harder task. And the smaller models are stuck altogether, but the larger models are able to solve the easier

task but not the harder task. And so they think they've solved the easier task, they think they're done, they output that answer, but that's wrong and really they should be focusing on the harder task overall. An example of this, one that kind of fits in this pattern is the negation thing where it answers the... surprisingly negation is a pretty hard problem for the language models. The easier task is just answering the base question and then the harder task is to answer the base question and then negate it. So we see this kind of this pattern there. Models really do love to repeat stuff. This is one of the other ones that I've been mentioning throughout. They do like to repeat

quotes that they've learned or to repeat patterns. These kinds of things are very very common. And another is that I think we might see on some of these tasks performance start to get better again. So this is this kind of idea of u-shaped scaling. So this is not certain to me yet, I have not been convinced that this is guaranteed to happen, but I think there's some hints that as models get bigger they'll start to, for example in my example with the harder task and the easier task, they may get big enough to be able to solve the harder task

as well and then their performance will start to improve again. Interesting. That sounds a little bit like the Dunning-Kruger effect idea, basically, that you start out not knowing and think you're knowing, then you go through a sort of trough of disillusionment where you realize, oh God, I really don't know what I thought I knew, and then by learning more, you actually start aligning your internal expectations, let's say, to your actual performance. Do you think that may actually be the case? Something similar to that with size and language models? That's an interesting parallel.

I hadn't thought about that before. There's something about overconfidence in language models often where they think they know that the answer seems clear and then is very wrong and it's overconfident on the wrong answer. So yeah, maybe there's some parallel there. Do you think that introducing more inductive biases and actually a little bit more of predefined structures or different more purposeful architectures would make some of these problems go away or at least be alleviated somehow? Yeah, this is an interesting one to speculate on. I think there's this idea of the bitter lesson. Have you heard of the bitter lesson?

I haven't actually. It's this article by Rich Sutton about how one of the things that makes AI progress in the end is just making better ways to absorb a bunch of data and a bunch of compute. So people try to handcraft lots of intricate features or make lots of clever architectural improvements and and they work a bit at the time but then eventually they get beaten by uh some system that can just absorb a lot more data and a lot more compute so like neural networks are an example of that and then transformers are like a refinement of that idea um so i'm i'm unsure if uh kind of adding a lot of detail in architectural improvements will outperform just making them bigger. I think it's quite an important question. Are there architectural improvements we can make versus just waiting for even more compute? In one way, it seems like it would be nice

to be able to kind of put more of our own structure on these systems and to kind of guide them a bit more rather than just making them bigger and seeing what happens. It's another question of how competitive that will be with continuing to scale. It's actually a key question and a key divide, I would say, in approaches in AI today.

And by extension, I would also add it's also one of the great philosophical questions around how to approach intelligence, really. So yes, I wouldn't really expect you or anyone else for that matter to give a definitive answer. I was just curious what you think on this. Okay so since we're close to wrapping up I guess then the last set of questions I had to do was really had to do with well what's the next steps really so you know first of all when are you going to go public with your results and what happens afterwards? Are you going to have another round at some point? And if yes, how can people get involved and potentially contribute to your effort as well?

Yeah, so our results for the second round should be going public in the next couple of weeks, so mid to late January. We are then intending to write a paper about the whole thing, detailing the winners and the lessons learned and doing some further analysis on the prize-winning submissions. Not going to rule out further rounds of the prize if there's more interest. We'll see how that goes. We'll see how we feel after having analyzed the results further and kind of gauging whether there is interest for a further round, more submissions to the contest, or similar contests in the future on related topics. Okay, well, it sounds like, well, first there was sufficient interest, I would say. I mean, the number of submissions that you got is encouraging, even though there wasn't like a big

spike, let's say, between the first and the second round, still you got a fairly decent amount of submissions and it also sounds like you didn't reach any definitive conclusion, so that leaves the door open for further investigation. So I think it was a very interesting effort that you undertook. And so first of all, thanks for doing that and for sharing your results as well. And well, if you're interested in keeping it up and improving it in some way, I think, well, there may be interest from the public as well. Yeah, that's great. Thanks very much. I hope you enjoyed the podcast.

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