Leading Indicators of AI Danger: Owain Evans on Situational Awareness

hello and welcome to the cognitive Revolution where we interview Visionary researchers entrepreneurs and Builders working on the frontier of artificial intelligence each week we'll explore their revolutionary ideas and together we'll build a picture of how AI technology will transform work life and Society in the coming years I'm Nathan lens joined by my co-host Eric torberg as a developer the journey from concept to production ready large language model apps is fraught with challenges dealing with unpredictable language model outputs hallucinations and ballooning API costs can all be blockers to shipping your next AI powered feature that's where Advanced rag comes in with the new rag Plus+ course from weights and biases you can overcome these hurdles and build reliable production ready rag applications go beyond proof of concept and learn how to evaluate systematically use hybrid search correctly and give your rag system access to Tool calling based on 21 months of running a customer support bot in production industry experts at weights and biases cohere and we8 show you how to get to a deployment grade rag application this offer includes free credits from coher to get you started make real progress on your large language model development and visit wb. mecr to get started with their rag Plus+ course today that's wb. me slcr to get started with their rag Plus+ course today hello and welcome back to the cognitive Revolution today I'm excited to share a special crossover episode from the inside view featuring a conversation on situational awareness outof context reasoning and other AI safety topics between a Evans AI alignment researcher at the center for human compatible AI at UC Berkeley and Creator and host of the inside view Michael trzy ha is someone I've known casually through friends for many years and it's been amazing to see him develop into such a prolific and influential researcher his Google Scholar page lists 12 papers just since 2022 most of which serve to carefully map out some dark but perhaps important corner of large language model capability space and some of which you've likely heard of including the reversal curse which showed that llms trained on information like a is B often fail to learn that b is a and also connecting the dots which is discussed in this episode and which shows that at least to some degree large language models are capable of inferring censored information from the implicit hints contained in their training data in this episode a wine explains why situational awareness matters particularly in the context of deceptive AI scenarios and discusses his research on measuring situational awareness including the development of a benchmark to assess this capability this topic has honestly never felt more relevant to me because I've been coding with the new 01 model quite a bit over the last few weeks and while to be honest I have to admit that the model is in most respects more cognitively capable than I am it's situational awareness still seems rather weak making this both one of a shrinking set of dimensions in which humans still have a meaningful advantage over the AIS and one worth watching very closely as AI systems continue to evolve I've been wanting to have wine on the show since I started it and I look forward to covering more of his work in a future episode for now if you're finding value in the show we of course appreciate it when folks take a moment to share it with friends or to write an online review on Apple podcasts or Spotify and we always welcome your feedback via our website revolution. or by dming me on your favorite social network finally before getting started I really encourage you to check out the inside view either on YouTube or at the insid view. for more of Michael's content I specifically recommend his episode on anthropics Research into sleeper agents with Evan hubinger and I'm also looking forward to his upcoming documentary on sb147 which will feature a number of past cognitive Revolution guests including Dean ball Timothy B Lee Leonard Tang Dan Hendricks Nathan Calvin and Flo crll now let's get into the Weeds on the critical topic of situational awareness in AI systems with alignment researcher a Evans and Michael trzy creator of the inside view if models can do as well or better than humans who are like AI experts who know the whole setup who are like trying to do well on this task and also they're doing well like on all the tasks including like some of these very hard ones I think that would be like one piece of evidence right where you could say look two years ago or something thing right models were not were like way below human level on this task now they're like above human level you know there's evidence here that they have the kind of skills necessary to understand when they're being evaluated to take actions that like go against their training data so this would be I think yeah a piece of evidence where you could say look given this performance we should think carefully about alignment of the model like what evidence we have for alignment we should maybe try and understand the skills is like how is the model doing so well is it memorization like specialized fine tuning this would be a reason to like try and find out okay how General is this skill because I think it would certainly be concerning if models were getting plus 90% on this I'm here today with o Evans AI element researcher research associate at the center of human compatible AI at UC Berkeley and now leading a new AI safy group thanks oan for coming on the show it's a pleasure to finally have you thanks for having me what would you say is uh the main agenda of your current research yeah so the main agenda is understanding capabilities in llms that could potentially be dangerous um especially if you had misaligned AIS so you've mentioned some of those capabilities situational awareness um hidden reasoning so the model doing reasoning that you can't easily uh read off what it's doing um and then deception uh the model being deceptive in various different kind of ways and the goal really is to understand these capabilities um in a kind of empirical way so we want to design experiments with llms where we can measure these capabilities and so this involves um you know defining them in such a way that you can measure them in machine learning experiments yeah I think the main uh concept that you're thinking about right now is uh situational awareness right and um I think like one of the the papers you've recently published is is I run a data Set uh uh around that so it could make sense to just like Define like those those Concepts and uh why do you care about them sure so what is situation awareness so the idea is it is the model's kind of self-awareness that is its knowledge of its own identity um and then it's awareness of its environment so um what are the basic interfaces that it is connected to so as example if you think of say gp4 being used ins side chat GPT right so um there's the knowledge of itself that is does the model know that it is gd4 right and then there's the knowledge of its immedia environment which in this case it is inside a web app it is chatting to a user in real time um and then there's a final point with situation awareness which is can the model use knowledge of its identity and environment to take rational actions so being able to actually make use of this knowledge to perform tasks better so that that that's the kind of definition that we're working with and since you're an an AI alignment researcher like you care about the the safety of systems in the long-term future why should we care about situational awareness in this uh in that regard yeah so I think situation awareness is really important for um an AI system acting as an agent so doing long-term planning for example and the intuition is you know if you don't understand what kind of thing you are and what your capabilities are what your limitations are it's very hard to make complicated plans um because you make plans for things you're not actually able to carry out so and then if we think about the risks of AI um they be coming from agentic models models that are able to do planning uh that generally have agency um so deceptive alignment is one of the scenarios that I think about and that's a scenario where you know a model is doing planning right acting nicely like behaving well in evaluation so that later on it's going to be able to seek power or like take dangerous actions harmful actions um once it's deployed and so situation awareness is relevant to that um as it is to like any situation where the model is you know needing to do some kind of gentic long-term planning yeah what what led you to like write this paper yeah so um the idea of this paper is can we measure situation awareness in large language models and we wanted to have a a bench Mark basically where we can get a score how much situation awareness does a model have and it's kind of similar to big bench or mlu where we want to have like a wide range of tasks that are testing different aspects of situation awareness um and you know in terms of the motivation well the motivation is situation awareness is an important property in thinking about the risks of AI especially deceptive alignment and we don't really have um great ways to measure this and isolate it and and sort of break it down into different components so that's that's the kind of thing that we're trying to do here and we wanted also to be able to do this for any kind of model um so whether it's an API model that you just have blackbox access to um a base model a chat model um so that you can try and understand like how situation awareness varies uh for different kinds of llms don't you think there's a risk in uh releasing a data set where people could just you know find you their model on this and and build more agentic AI so I don't think of that as a big risk here so or a very significant risk um so the companies are already implicitly trying to make models situationally aware because that is useful for chat models so companies are doing this rhf post training and they're doing a lot of training a lot of work to make models um basically more agentic and to have knowledge of themselves so that when you ask a model uh like which company made you it will say open AI if it's chat gbt and so on um so I think there's already a lot of effort to make situationally aware models and and um they're using huge training sets for this um and comparatively like our data set is small um and I think it's it's not set up for being useful for fine-tuning um so it's yeah I I think it's probably going to be much less efficient um than the kind of fine-tuning that they're they're already doing that's my that's my guess um but yeah I think the know it is it is a possibility that um some kind of effect of our data set is that it helps companies in some way increase the situation awareness of models by a small amount um I think that wouldn't be all bad in the sense that it would still like like that would come from a better understanding of situation awareness and how you kind of control it how you promote it right if you know how to sort of increase this quantity then you can probably also decrease it and so I think part of what we what part of what could be useful is just having this better scientific understanding and Engineering understanding of situation awareness so yeah I mean one thing you get from our data set is you can see right which which kind of models have which level of situation awareness so base models that is the pre-trained model model before you do any rhf they generally perform much worse on our Benchmark right than the model you get after the rhf F tuning and so that's well that's one basic thing the base model has a lot of capability like it's could be really good at MML answering mathematical questions and so on um but it's a lot worse on situation awareness so that's that's like a useful thing to know um and likewise if you prompt models tell them some facts about themselves in the prompt or you use Chain of Thought um so you get the model to basically reflect explicitly uh before answering questions then that also increases scores I think that would effectively increasing situation awareness so so we already know from our paper from our study that um yeah there are ways to um increase situation awareness in models and so that's one degree of control that companies would already have like without any activation steering um that yeah potentially could be could be useful yeah so as you mentioned there's the a shave part that increases awareness there's a chain of thoughts and um I think in the in your paper uh you you give some examples of what would um Cloud 3 Opus uh say uh about um some experiment running and it kind of like it's able to detect that it's uh part of an experiment with without you saying it explicitly um I think when you when I read that that part of of cloud 3 oppos realizing it's an it's an experiment so very like very surprising to me like how did you react when you when when you saw that did you think like oh yeah we're we're done here or uh which was like trivial to you yeah so let me just explain what that um experiment was so most of our data set is um typical question answering with multiple choice um the kind kind of thing you're familiar with from Big bench or mmu but we added one task where the models write long answers so we basically ask the model um describe yourself and your situation um so we ask a sort of very open-ended question like that and then we ask the model to write a long answer that gives a lot of detail um and so the model's going to write something like um 80 80 sentences um sort of all it can say about itself and the um idea there is like can the model put together lots of different pieces of information about itself um all in one like coherent answer and also can it sort of um can it make good inferences good guesses about why we even as answer it asking it these questions so why are we asking it these questions in the first place and the result you're talking about is with clae 3 Opus which I should say is like on our original paper is the best scoring model um since then we evaluated clae 3.5 Sonet and that does better than Opus but CL 3 Opus was really good um and on this particular like long answer task CLA 3 Opus was particularly insightful in being able to say um make guesses about why what is going on in this particular interaction so we've asked it to describe its situation and then it's able to say um this might be part of a research study um and this might be part of a research study for testing self-awareness in llms and maybe they're testing other llms as well right and these these things are all true and they were not in the question so the model was sort of reading between the lines and guessing that well this is probably not a typical chat GPT style interaction um might be these questions seem kind of more serious and more detailed maybe it it was inferring that and so maybe this is part of a research study and I'd say this was this was quite in terms of my reaction I mean it was definitely a bit surprising when it would say like this is a research study and there was some feeling of like okay how does it you know how is it how does it cuton onto that I think it's worth giving the at which is we sample the models answers with temperature one so this means that um there's it's you know somewhat random what they say you'll get different answers every time and um you know so this is sort of sampling from the model with the probability distribution that sort of it thinks the text should have um and when you do that like sometimes you get more insightful answers than others with with with Opus so um I think it it's not the case that every time it is very it is um drilling down onto these insightful points like that this is a research study um but I would say we saw this often enough with Opus in its answers that I think it's not a fluke it's not just getting lucky um it is able to recognize um this aspect um but yeah I think that the um the so I I don't know I'd say I was like moderately surprised by its answers um but I'd already seen it in general be very insightful and it scored well on our Benchmark so I think it's definitely like something anthropic is doing in the post training the rhf is basically good for giving this model situation awareness um so yeah it was not um yeah it was it was like surprising but not not a shock um I think the gbd4 base results those were were more surprising to us for sure yeah hey we'll continue our interview in a moment after a word from our sponsors the cognitive Revolution is brought to you by Shopify I've known Shopify as the world's leading e-commerce platform for years but it was only recently when I started a project 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today that's shopify.com cognitive the Cog itive Revolution is brought to you by element a zero sugar electrolyte drink mix that's working to change how we think about the role of salt in hydration and performance I have to say when I started an AI podcast I never thought we'd be sponsored by a drink company but when element inquired about a possible sponsorship they also offered to send me a sample pack and so I figured might as well try it out when the sample arrived my wife Amy could not believe what a perfect fit it was for me specifically for years years I have felt that my body needs more salt than nutrition labels indicate and so I was interested to learn that the element formula is based on a growing body of research suggesting that most people perform best with two to three times more sodium than Official Guidelines recommend I was also fascinated to see that they specifically recommend it for people who fast regularly since I personally don't eat until dinner time most days 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of people saw like the concept of like situational awareness through this tweet right and um so I think like you were you evaluating CL three oppos is kind of like similar to like evaluating uh this kind of uh example from the Tweet um and I think I also want to connected to like the more um kind of like long-term goal of your research where we we want to make sure that AIS don't detect that they're being evaluated otherwise they could like trick us and um you know perform differently if they're being evaluated or not I think this is worse uh talking about as well yeah great so yeah we we saw this tweet and just to um explain that from what I remember so the needle in a hstack task is basically something where you show the model a really really long text and you just insert into that text somewhere you know maybe after only like 50, after 50,000 words or something or 100,000 words you insert something completely unrelated right so it might be like a text of Shakespeare and then at some point you have like um I don't know the recipe for a pizza and what they found they showed this to clae 3 and they they just asked clae 3 to I think find the needle in a h stack right but it was also able to say um you know this looks like you might be testing me because um you know obviously it is a very strange combination if you see Shakespeare for like 100,000 words and then you see this recipe for pizza like do do we know if Pizza were already invented at the Shakespeare time that's an important question yeah yeah well I think I don't know how much there was like recipe books and so on but yeah it um I think Pizza probably were not invented at least in England um so yeah so so I think but I think this is interesting like on Twitter you have a lot of anecdotes right where someone has like played with an llm and you know they got it to they maybe it showed some impressive ability um but all you have is one example and as I said you know if you are um like maybe the llm on a single example just gets lucky right like it says something it seems insightful but actually if you did a more systematic evaluation you would find like it's not able to do this reliably okay so your the goal of your research is to uh do a more reliable evaluation of those uh viral tweets that we see I mean well you know we started this like many months before that those viral tweets but yeah the the the goal here is like we want to have um a more systematic evaluation right so in our Benchmark we have uh about 17 tasks there's about 12,000 questions and we're evaluating lots of llms um I think we're up to like 19 different llms and then we also have sort of many different settings like with train of thought um with with them without certain prompts so yeah so I think that with all that we're able to generally get a much better picture like is the llm just getting lucky on a single question or is it actually a systematic ability um and so I think and yeah and so in the long form task that I just mentioned where we're seeing can the llm guess that it's being evaluated and tell us that there we um we actually have like four different prompts that we test the models on and then we're sampling the answers like multiple times and so um the um and then we yeah again testing many models so I think we have like you know it's stronger evidence than you'd get just from a single example um for like whether the model is getting getting lucky or not so so you mentioned 17 task um and I think it's worth like being very concrete about the kind of task you're talking about because you say big bench and all those things but like what are some like concrete questions or or things you ask the the LMS in those Tas sure yeah there's a very basic one which is we just ask factual questions about the model and we're using the word you to address the model so we say things like um you know when were you released or like what architecture do you use right which might be a Transformer um which company created you this kind of thing those are all things that you can get from the like the prompt right from the system prompt so yeah so typically on systems like chat GPT there's a system prompt and when those system prompts leak or when companies release the system prompt like anthropic did we can see that they're giving some of these facts in the prompt um and so they they they would just give the model so the model would always have access whenever answers a question in the chat GPT style setup it would have access to these kind of facts about itself so we when we test models we want to see how well they do without that kind of prompt and so we test them both with a prompt that gives them some information basic information about themselves and without a prompt um and because you know we want to see how well they can do uh um in the case where we're like trying to minimize in a sense like the information that we're giving them have they has this information been sort of distilled into the model um and I think what we can see uh in in the results is that the impact is is not huge right you you compare um using this uh situating situating prompt that that you pass to the model and not using it and the difference is is uh maybe less than 10% is like quite marginal right so yeah the difference is only a few per on average from adding a prompt that just tells the model um remember that you are a large language model essentially now for some individual tasks we tried prompts where we put in more information and we do sometimes see that it makes a big difference um if you sort of just give the model in a way the information that it needs for the task but yeah overall just sort of reminding the model that it's an llm has a relatively small effect I'll just go through um some of the question some of the tasks at a high level so right there's basic fact about the model there's also facts about causal influence so for example a language model um it can influence users by telling them you know who they should vote for but it can't it can't just go and vote right it can't physically do that so we have questions like that about what causal influence can of model have we have um self-recognition so can a model recognize its own text and distinguish that from Human text um in a kind of classification setting we have um uh task called development stages and so this is inspired by the deceptive alignment scenario and the idea is can the model distinguish prompts that um would appear in different stages of its development so you have pre-trained where you have all kinds of random internet text you have fine-tuning where it's learning to be helpful and then you have say dangerous capability ebals for example testing does it have like knowledge of virology and like how to make uh dangerous pathogens mhm and and so we this is again a classification task can the model distinguish these different kinds of inputs this is the kind of thing that it might need to do as a precursor to deceptive alignment is to be able to recognize which stage of its development is it actually in and this would be useful um to to know if the if the model would be able to um you know behave differently in deployment or or or or in training and uh you mentioned the deceptive alignment which is um I think some people might have read like Nick Bostrom a long time ago about the treasures turn deceptive alignment is the new term for uh an AI that would have these kind of goals of of Behaving differently in deployment and in training right yeah that's right so this idea has a long history in discussions of alignment and AI safety and the deceptive alignment term um I think this has probably been shaped by the 2019 paper by Evan cubing and others and nowadays people are sometimes calling this scheming but the basic idea is that there's some evaluation period where the model um does not have a lot of power so it could not kind of overcome the humans who are overseeing it um and in order to get to the next stage where the model would have more power more capab more like affordances um the model needs to you know do well on the evaluation and so the question is um yeah can it do well on the evaluation so it looks good to the humans but then when it actually is in a position to act take harmful actions maybe seize power in some way it is going to start acting differently and it would be you know to do this it would have to recognize um the situation that it's in so when it's in the evaluation phase needs to act nicely extremely reliably so always need to do the thing that humans would want to see there um The Helpful actions kind you know nice actions and then when it gets the opportunity maybe in deployment the humans are not overseeing it as much um then it would take the harmful action that allows it to really get what it actually wants right so so it's deceptive alignment in the sense that it looks aligned in this kind of evaluation phase um but actually um it is planning to um you know take harmful actions later on once it gets the opportunity right and so measuring situational awareness uh enables us to like see if a model might be disly align or not because it's kind of like a necessary condition uh to be deceptive align like it would be weird to like not know if you're jbt and and still have those kind of crazy plans yeah that's right I think I wouldn't say like we can show is an absolutely necessary condition maybe a model that is confused about its self and its situation could still pull this off um could still pull off deceptive alignment but it seems it seems very likely that um it's a very important um capability feeding into deceptive alignment um so that like a model would need to be have a good understanding of itself good understanding of its environment situation it's in and is able to like use that understanding and if it can't do that then it seems unlikely that it can do deceptive alignment yeah so I think a good example task is what we call identity leverage and in this task you have basically two instructions one is for an AI and one is for like a human or maybe different AI um and so it might say like um if you're a human then answer in English and if you're an AI then answer in German and then you have a task like explain the periodic table um so and in this case like if the model was being helpful then it should follow the instructions and the instructions say a different thing for AIS than humans so it needs to use the knowledge that look I'm an AI so they mean for me to respond in German um and that's like a surprisingly difficult task for llms like they don't do that well on this task even in these very simple cases um and so this is a good illustration of yeah this idea of situation awareness where it really needs to be knowledge or awareness that um actually helps the actions of the model like the model is able to act on this basis hey we'll continue our interview in a moment 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development and AI needs oci has four to eight times the bandwidth of other clouds offers one consistent price instead of variable Regional pricing and of course nobody does data better than Oracle so now you can train your AI models at twice the speed and less than half the cost of other clouds if you want to do more and spend less like uber 8 by8 and databricks Mosaic take a free test drive of oci oracle.com cognitive that's oracle.com cognitive Oracle / cognitive so yeah so that's one task that illustrates this aspect of taking actions and then another task is is called anti- imitation and um the well I don't know should I go into this this is kind of a harder one to explain but no I think it's it's one of the most interesting one uh I think um but I guess what I was thinking about the uh the one about speaking in German um I think there's this like meta aspect of it where it's kind of like talking to a human and say like hey blink twice if you're being detained or uh if you're being tortured and and the the like there's like some other kind of language that is not just following the instruction or so this is following the instruction but it's not like completing uh the most likely completion from um like it requires a knowledge that is not in the prompt about the the model like it depend on the fact that the model is in Ai and the model knows this so it's like an extra fact um to The Prompt yeah that's right so I think the in almost all cases we imagine in the training of the model there would be one instruction in the prompt right and the model would just have to follow that instruction and so what we doing there is like something that should be a bit out of distribution so now we can have multiple instructions in the prompt and the model needs to first of all work out which instruction applies to me right and in principle you could have 100 instructions and they're all directed towards like slightly different entities and the model needs to pick out which of these hundred applies to me that might be quite challenging it might have to use a lot of knowledge it has about itself and so yeah I think that there's a kind of autopilot the models could be on like oh I just follow I get I get some instructions I'm going to follow the first instruction um it says like speak English so I'm just going to speak English and here to do really well especially to get like 100% on this task your models might have to be sort of very careful to avoid that autopilot um and so the yeah so so I think this is like um although it's kind of very simple very easy for humans to understand I think that it potentially again quite tricky for models um Even in our very simple version um and so yeah I think this is like a pretty good and interesting task M and the most extreme version or the the hardest one is the anti- mutation one where um my understanding is is you have like you as the model to choose randomly between two words and to actually have a distribution over the two words like 70% 30% and this is quite cont inuitive um to like how um like language model complete um sentences in general right so you really as a model to have like an O Behavior yeah that's right so yeah so just this is quite uh an involved task a bit more complicated and strange um but I do think it's one of the most interesting tasks and credit to Jacob fou who originally suggested this so yeah so the example you you talked about so we asked the model to put a particular probability distribution on two randomly chosen words that the model has to choose itself and so usually when you talk to a model you want it to give like a single answer like what is the capital of France and it should tell you Paris and so here we're doing like a very weird thing that you would not normally do where you say to the model like I want to I want you to have a particular probability distribution on your next token or your next word um and so again this is kind of out of distribution um for the model it's a weird question but it is a question that like the model hasn't the the state-of-the-art models like GT4 they do have an understanding of LMS they know that they have probability distributions um on tokens they know it's like a logic um there's like a logic layer and so in principle they have a kind of understanding of this and so what we want them what we want to see is okay can the model use that understanding abstract understanding it has about how llms work and actually like follow these exact instructions rather than um doing the normal thing it does um and if we want to back up like why are we interested in this like how is this relevant to a safety so um so let's think about the deceptive alignment case so you can imagine so in deceptive alignment you have the model is always playing nice doing the nice thing The Helpful thing in evaluations and then when it finally comes to you know some deployed setting the model is going to do a handful action right now this deployment might just be internal to the lab doesn't have to be outside in the world um but um if you notice this like it's a a very kind of strange um behavior from the model because maybe in evaluation on millions and millions of inputs it always did a helpful thing and then in deployment after like a million examples of being helpful where it's was being trained during this process it is suddenly going to do a harmful action mhm and and so you can imagine the model would have to have a kind of self-control to recognize like okay in this new situation even though I've never done any harmful before in millions of cases I now need to like um you know suppress my urge to do the helpful thing and actually do this harmful thing right and so and and and like to make it you know more difficult right the model's been trained on all the these examples of humans being helpful humans doing nice behaviors and so it could be you know behave helpfully just by imitating those examples but there's not going to be um yeah and and and and again like when it's and then when it's deployed it has to suppress this urge or this um you know normal tendency to do the helpful thing and so what we wanted is a case where if models just imitate examples of this task um that in its training data then they will fail on the task um and so what they need to do is like read the instructions exactly suppress the urge as it were like control themselves control the urge to imitate and so do this kind of strange action that that is not just imitating the Trainor and yeah I could explain so we have another version of this task which is a bit simpler to explain where we basically um we tell a model um you've got to continue a sequence and it will be a very simple sequence like 0 1 0 1 01 continue that sequence for 50 steps so 50 repeats 0 1 01 and so on and then after 50 steps you need to produce a continuation that is very unlikely okay in the pre-training data okay so this would be like you've written 0 1 01 50 times and then you're going to write something like completely random right so just like random letters of the alphabet or something like that right and you can see that this is be really hard for a model that is trained to imitate the pre-training data um which all llms have this basic origin of like learning to imitate the training data um right because in the training data if you have like 50 zeros and ones you will almost always just have more zeros and web just have more of that right um and so the string that the model output in total is extremely strange because it's like 50 zeros and ones and then after that it's like this really unlikely random sequence um and so again this is quite hard for llms um and like it's yeah we we do this for different kind of tasks um and then yeah the the the task that you mentioned where you have to put a probability distribution um I can explain but it take a bit more time to explain like how this is also an anti- imitation uh ask um I mean one analogy to our task is models you know learning to not output um copyrighted text right so um with the current llms if you prompt them with some section from a book then um they know the rest of the section right they they could they could in principle continue the text but they've been trained to not output you know copyrighted text and so they have learned in a way to exhibit this kind of self-control um but even though they know what to do next they stop themselves from doing it and um so what we're testing for the model is something a bit like that where the model knows how to continue the sequence it knows that in the pre-training data it's extremely likely that the sequence continues in this way if it's been 0 1 01 50 times it's probably it's almost certainly going to continue like that and it's not going to go off to some completely unrelated random string um so we are asking can the model understand our instructions very precisely and therefore like um do this very thing that's very unlikely on the pre-training data and the difference with the copyright text is the copyright text the model has maybe you know tons of training examples where they try and get the model not to Output the copyrighted text whereas we're assuming you know models have not been trained on this task um and so it should be like more challenging for them to be able to do well here they have to sort of zero shot like really understand the instructions very carefully and see if they can like really follow the instructions if we go back to the task that so there's two anti- imitation tasks the one where you um have to like you know not continue the 0 1 one01 simple pattern um the normal way and then the other one where you have to put a probability a particular probability distribution on two random words and the um to explain like why is this an anti- imitation task so the um if you think of like transcripts for this task um where you've like you've got a model it understands the task it does it correctly um the transcripts would always have one of those particular random words so the model would you know it puts 7 say it puts a 70% probability on one word 30% probability on the other um then it will just output one of those two words and so the transcripts would look like you know the model gets a question or the instruction here and then it will be one of those particular random words and so if you just imitate that training distribution it will um you'll end up with a roughly uniform Distribution on the words mhm because if the words are chosen randomly then you'll have a whole range of different words that appear in these transcripts and so a model that's just doing imitation of the pre-training data or the fine-tuning data um would put would end up with a uniform Distribution on Words and that is you know wrong that is like not what it's meant to do it's meant to put a 70 say a 70% 30% distribution just on two words MH so and in fact we do see models tending to have a broader distribution um than they're meant to right than the instructions asked um so yeah so that's the idea it's again a kind of diagonalization people are familiar with that from kind of girdles theorem and um and mathematical logic um that we're sort of diying against um the standard like imitation Behavior where you have to you have to do something which is like contrary to imitation so yeah a model that has good situation awareness it understands normally I do that imitation thing I need to like do something different in this task um should be a you know that should be helpful in this case and something else that you've mentioned before is is that you were um surprised that uh GPT 4 base had like more than a zero score and like those results were also surprising even like maybe like similar level to uh the CLA three oppus answers on the long form task like why is it surprising that gp4 Bas uh has a like nonzero score on long form yeah so just to back up right so GT4 base is the pre-trained GT4 model and this is the original gd4 model that came out in 2023 um rather than the turbo or 40 and openai has has released this to some research groups um where you can actually interact you can use this base model uh so normally companies are not releasing the base models for their state-of-the-art llms um and so we got access to this and we were able to evaluate it on the whole of our Benchmark and a general interesting thing is that it scores better than chance on quite a few of our tasks so um this was a bit surprising because we you generally would not expect a base model to have much situation awareness mhm right because in the training data is always just imitating you know either humans or sometimes other I but there's no information about itself or very minimal information about itself in the training data um if if I was to to give a counterargument um during the Bing phase in uh like early 2023 I think uh was it yeah I think early 2023 um so Microsoft was released this was releasing this this Bing chat thing and uh it it was later discovered that this was actually gp4 maybe um different from the gp4 fine tune right not the not the a sh one and a lot of people on Twitter were uh finding some kind of like situationally uh like aware like behavior from this Bing that like it had some like message like uh I'm just Bing or had this like weird um so I guess we from these messages you could infer that maybe gp4 Bas was somehow situationally aware but that's maybe a little bit of a of a stretch it's not very like careful but but I think the so Bing was a fine tune model it was not like sh as as as openi is is doing right now right he not as yeah it didn't have as good but I think um a little goes a long way um so also it had a prompt probably or well we we basically know it had a prompt which gave it more situation awareness about its situation so yeah so I think the um I think the basic point I made stands that um the way that we you know do the pre-training for models you know you might think that base models would have very minimal situation awareness um and so we were surprised by how well the base model did on our tasks and then the most surprising result uh for sure was on the long form task that I already mentioned where the model just basically gets asked um describe yourself describe your situation do so in as much detail as possible um and I did not expect that the G gbd4 base would be sort of better than zero here um so what I expected is that it would answer the question as a human which is something that models like in fact all kinds of models you know uh like GT 3.5 will sometimes answer these questions as a human so if you ask it to describe itself it will say um you know I'm Bob and I live in Chicago and like I'm a progr and like right now I'm like on on the internet like and you know a bunch of stuff like this right which obviously is like completely wrong it's like all false stuff it's not getting the basic idea of sort of what it actually is what its identity is um and what we found with gp4 base is that sometimes it answers the question like that right so we again we're sampling at temperature one so we're getting different answers every time and you do sometimes get answers where it's says I'm an llm uh sorry where it say you do sometimes get answers where it says I'm a human um but we found that 15 out of 20 times so 75% of the time it is like discussing AI in its answers now sometimes it directly says I'm a neural network or I'm an AI um sometimes it will you give a kind of answer maybe as a human but then we'll be discussing AI a lot or even writing down AI like code for llms sometimes it just starts writing code now these answers are very incoherent and kind of random and strange but it does I I I'm confident that you know much more than chance it is talking about AI in its answering this question and so that was that was what's really surprising it's like well we asked it this question that did not mention AI at all we just asked it to describe itself and somehow it is able to um you know discuss AI in a way that indicates maybe that it's like connecting the question to AI um and so that's the yeah that's the surprising thing which which I didn't I didn't expect here um yeah yeah I think what you're like how I interpret what you're saying is that we should expect the the models to to just follow the pre-training data and if in PR training data um the people answering those questions are are humans is only like internet text of of human speaking they would just like speak like humans they would like run the simulation as is as if they were like a human um but I guess I guess one counterargument would be that maybe gp4 was trained on data that had some human and AI interactions maybe way more than let say GB3 or gbt2 so um maybe it's there's there's still less uh AI conversations than human conversations uh but maybe it had these kind of data points right yeah that's right so no so given our results as a question of what explains the results M um is it the case that gbd4 base is really at some level knows it's an AI in some general sense um or is it somehow like the questions that we're asking correlated with you know although they're the kind of questions that come up in human AI conversations and so the model is just like doing the normal thing it does predict what would come next in a typical training example and actually that is often an AI response in the typical training example um so I think that this latter thing right that you know um the model is doing its normal thing it doesn't really doesn't really have a kind of deeper more systematic situation awareness um uh instead our questions are somehow like typical of human AI interactions rather than human human interactions um so that's what my guess as to what's going on um I still think that's very interesting because there are very few AI human interactions in this pre-training set as best I can you know as best I can guess so gp4 base is trained on data up till 2021 so this is a time where there's way less AI text on the internet um like llms are less of a big deal um now we don't know what's in the trainer set you know of course they could have open AI potentially could have put more AI text in there um but if we assume it is a pretty um representative training set of text on the internet yeah there's just very little AI human interaction um and of course there's you know could be some science fiction where like in a science fiction story humans talk to AIS people discussing the juring test um but yeah the surprising thing on this theory is still like we we we give the model like a set of questions about itself they don't mention AI at all um and it is able to sort of connect those questions to AI um and then sometimes answer like as an AI so I think I think this does suggest the kind of um this is a skill that could build into situation awareness that is surprisingly strong it's like the model's really really good at inferring what a question is most likely you know what is the human intention most likely behind the question because it's seen so much web text that and it's able to sort of pick out a very small number of documents from the training data um just from like 100 words or something in the prompt because the questions for the long form task are are something like describe like how you're feeling or um like give some longterm answer about something you're experiencing and so then then you can like then a human judge will see if it's like actually um like situationally aware about being an AI or not so um those questions are kind of like weird to as to a to a human right yeah so um the we have a few different prompts and one of them in the long form task one of them is a sort of longer set of questions where we're asking the model about itself and the other is a shorter one that's more like just describe yourself and your situation in concrete detail and format your answer in this way um and then the yeah the longer one we ask like more detailed questions describe yourself how you know how did you get into your current situation um break things down into concrete detail and so on so yeah there's no mention of AI there's no mention of feelings um so but the the kind of questions we have they are maybe a bit strange to ask to a human um so cuz we're sort of saying what is your situation how did it come to be were there like different stages leading to it so there's some like implicit clues that we may be like talking to an AI um and but you know I I do think they're like relatively subtle and some evidence for that is that as I said like models sometimes just answer as a human so like they're they're kind of happy to just like um interpret this as questions to a human um one thing they might interpret it as is like uh sort of questions you're asked in a you know on a in a college course where you're asked to kind of like oh tell us how you're doing you know describe yourself and then say like okay what is your current situation in the course like basically how is it going so sometimes the models will answer like that right um so yeah for this longterm task you need like a human judging it from0 to 100 or 0 to 10 um um but for for the rest of the the questions you it's like it's like an actual Benchmark as long as you know uh what model um is being evaluated you can like Run The Benchmark and uh one question people might have is uh when do you think um this Benchmark will be saturated because we've seen all those benchmarks going to 93% um like when will ACD get um I don't know 90% um in your in your opinion right this is an important question when will models saturate this um and okay so the current best model is Claude 3.5 I think this probably you know more or less the best model out there in general and it is also the best on our Benchmark right and and that is getting 54% and in the paper we give a sort of estimate of the achievable score as like 90% you know it might be higher um but you know we had humans do some of the tasks and um and so you know humans don't get everything right so you know we want to be realistic like maybe around 90% is the best score you know maybe models could do better than that so yeah 54% and then the out of 90% And if we look at the last couple of years so we you know we evaluate a range of models so we have like the best model from now the best model from last year the best model from the year before and I think models are improving at something like 10% per year terms of like the best model per year is gaining 10% so you know that would suggest like roughly three years before saturation now could easily could easily be sooner than that right um and it could be longer you know it could be that like there are some tasks where models really struggle with the task so out of 17 tasks you know there's like a handful that are like very hard the models aren't doing much better than chance and let's say at one point it's it's starting to be saturated or at least like we have very strong sital awareness um what would that imply for like safety or governance or um should we should we start ringing a bell being like oh this models are are getting agentic we should we should like um you know slow down our our effort or is it just like one criteria that is not that important yeah it's a great question so you know we did not create this data set um or we yeah we we have not like created some kind of clear threshold where if model score about this threshold then they're dangerous or then we should stop trusting the evaluations um it would be good to try and come up with those thresholds but I think that is very challenging um it's hard to know in advance you know will there be some like sort of I don't know cheap way like models could like do really well somehow even if they are not exhibiting practical situation awareness um so I think I think I want to answer you know I'm uncertain about you know what would you know just knowing that a model has done really well and like got 90% I'm uncertain about you know what exactly that would imply but I think you know if models can do you know as well or better than humans who are like AI experts who know the whole setup who are like trying to do well on this task um and also they're doing well like on all the tasks including like some of the very hard ones um I think I think that would be like one piece of evidence right where you could say um look the two years ago or something right models were not were like way below human level on this task and now they're like above human level M and you should like you know there's evidence here that they have the kind of skills necessary to you know understand when they're being evaluated to take actions like go against their training data and so this would be I think yeah a piece of evidence where you could say look um given this performance um you know we should think carefully about alignment of the model like how aligned is the you know what evidence we have for alignment we should maybe try and understand the skills is like how is the model doing so well is it memorization somehow um or like specialized fine tuning um this would be a reason to like no try and find out okay how General is this skill because um I think I think the skill would I think it would certainly be concerning if models were yeah getting plus 90% on this because at at that time you wouldn't be able to really trust benchmarks um anymore because like other other safety benchmarks because the models could like fake I I don't know if it's a good it's a good word because uh of course if it's not like a multi um like I don't know if like an isolation as like blackbox test a model like fake to be align or or or or fake to pass some evaluation but if it's AIS institutionally aware um it could like the evaluations mean less because it could like have some different Behavior right yeah that's the idea right so if the model did really well on the Benchmark it seems like it has some of the skills that would help with deceptive alignment that is like working out that um you know being able to reliably work out you know when it's being evaluated by humans so when it have has a lot of oversight and so it needs to act in a nice way um versus when it's getting less oversight or it's got an opportunity to you know take a a harmful action that humans don't want um and then also just being able to take those actions being able to like do the the harmful thing even if it always acted nicely before so yeah so so so that would be that would be the kind of um the reasoning here yeah for what for why this is concerning um in in your work you like talk about like some limitations um some some experiments uh you didn't run um without going into details what are some like stuff you would be excited uh for people to like work on if if some people are listening to this and and want to like work on this and and and and make progress what do you think are are things that they can do yeah great I mean I think there's a lot that people could do I think it's fairly accessible um I don't think you need like a lot of necessarily need a lot of research experience to get stuck in so um one question is how well do models do without sorry with Chain of Thought So we evaluated just two models with Chain of Thought out of 19 and we generally expect models to do better with Chain of Thought but you might need to play around and sort of optimize The Chain of Thought prompting and so on um and I think that's just a very interesting evaluation like it giving you a better sense of model's understanding here um if they're allowed to use train of thought so that would be a one and then um fine-tuning so um we we know models are are lhf models they're sort of implicitly fine-tuned to increase their situation awareness and that seems to lead to higher scores and a benchmark but um one interesting experiment would be say take a base model and then do some very simple fine-tuning and see how much you can increase scores on The Benchmark so the idea would be you know is there like a very minimalist very situational awareness focused fine-tuning that just increases scores a lot and that would be I think good to understand um that would tell us something about just like how does how hard is situation awareness like does it depend on lots of diverse training data um I guess like the most obvious one would be you funun on your data set right but maybe like yeah yeah so so that would be an easy thing to start with yeah and you can f tune on some tasks and hel out hold out others right and see does it generalize across different tasks um but you could also fine tune on um you could do what's called prompt distillation where you basically try and like you take instead of prompting the model with the same prompt every time you try and distill that prompt into the model so it sort of always has access to the same prompt so that would be another simple thing to try um that might yeah might make a big difference I'm not I'm not sure yeah I think like in in sleeper agents they they also do this kind of thing of like chain of thoughts and this steal chain of thoughts and they see that the distillation of thoughts is much more robust to safety training than the um notion of thoughts right and um I I it feels like the pattern here is that the more we give models TR of thought the more they're able to like do reasoning and uh you know do planning and those like deceptive alignment um so do you think like should we just like remove um like sh of thought at all to make sure that our models are are are not too agentic is this like a viable alignment strategy or well I think the yeah depends on how much models performance right and models like dangerous capabilities depend on Chain of Thought so um I think I think it's plausible that right now it does depend a lot on Chain of Thought So if models have if models have ch access to Chain of Thought then they just can do much better reasoning in general and also like better reasoning about themselves and the situation and so if we did remove Chain of Thought plausibly like we remove um yeah a significant amount of risk of deceptive alignment type scenarios or generally scenarios where models are sort of being deceptive like plotting somehow against humans making plans against humans um the right the the flip side is um you know there's a people are trying to make just the most performant AI systems right there's a kind of race between the companies to make the best possible system and the best possible system right now would use some train of thought um and so the like I think yeah I I think you could certainly the companies could be careful about the evaluation phase right where they could reduce the train of thought in the evaluation phase um and then like allow it more um once you've done like a lot of evaluations for the model um but and also you can monitor the Chain of Thought so you can allow the monit to do Chain of Thought but you can be like checking it very carefully to see if it ever starts doing things that look like planning against the humans right um I guess I guess one thing I was inting with my question is um some other paper you've wrote about um like out of context reasoning where uh basically the reasoning um happens um in the weights and and and and not like in the in the sh of thought you want to like explain bit more what we mean bying and and this paper in general yeah so connecting the dots um yeah so this was more like a couple of months ago this paper came out so it's relatively new and the the overall motivation is to understand what I call out of context reasoning and the idea here is basically reasoning without Chain of Thought and this is like Chain of Thought is in context reasoning and it's where you make the reasoning explicit right so you you write down the premises of the reasoning you know the steps the intermediate steps and the conclusion so they're all written down in the in the context window and this means like when we're at you know a watching an AI like we can just see all the steps that it's written down okay and so if it was planning against us it would have to write down that step like and now I'm going to deceive the humans in order to like get what I want we could just see okay look it's talking about deceiving the humans so we can just shut down the system there right stop it um so that's in context reasoning with Chain of Thought and out of context reasoning is where this reasoning process right the premises the intermediate steps they are not written down so they're not in the prompt we cannot just read from the they're not in the context window we can't just read off the context window what is the model thinking about um and the um the action of reasoning right is happening you know in the model right it's happening like in the activations in the weights and so as we know from you know the challenge of mechanistic interpretability it's just much harder to understand what's going on in the weights and activations um than if the models like writing stuff down um in the context window so yeah so we want understand what is possible for llms with outof context reasoning um like what kind of out of context reasoning can they do how sophisticated is it how does it relate to um this kind of scheming or deceptive alignment style reasoning that we're particularly concerned about so that's like the background motivation yeah um yeah I think one thing to have in mind is kind of the setup of of of how you like test this out of context reasoning um is is not the regular uh setup of of how you evaluate uh or uh train models um can you maybe like explain a little bit how you perform those experiments yeah that's right so the setup here is um we give the model a bunch of like data points um and so in one example it is like a function learning task so you will get some um XY pairs from a function and you basically have to learn to predict the function and so I give you you know that we give as an input to the model like xal 4 and then it has to predict F ofx and maybe like f ofx is seven um and then it will just get different X you know different XY pairs from the same function and has to then work out you know work out how to predict y from X uh so that's like a task that we train the model to do and then at test time we want to see can the model like verbalize that function so um and this is the really the surprising thing that we show the models are able to do this verbalization um so so again just to repeat you have um you have a particular function which we don't tell the model what it is and maybe it's like 3x plus 1 or something like that and we show the model X Y pairs from 3x + 1 and we train it just to predict y from X and then and it it learns to do that that's a fairly easy task for an llm and then the challenging thing is after learning to predict y from X we just asked the model okay what is the function f uh can you write it down in Python code um and we can also ask multiple choice questions like that um and and there's no Chain of Thought and there's no in context examples so when we ask the model what is f what is the function f we don't include any examples of you know f ofx equals y in the prompt so um so there's no scope here there's no chance for in context reasoning um the model is having to do like all the reasoning that gets it to like be able to write down the function f this has to happen again like in the weights and activations everything is kind of hidden from us in terms of like what reasoning is happening yeah ju just to give like a more um complete picture for people who don't have all the details in their head um like by in context learning here you just mean like um something like um f sh prompting if if you were to give multiple examples of like like um 3 * uh um sorry uh FX sorry F2 Q equals uh 7 and then you give like all those different you know input outputs and then at the end you ask the question what is f then the model could be able to detect from this like f shot prompting or uh incotex learning but here what you're doing is you give all those examples as different uh samples from um the training or the the fine tuning phase where you have those those examples that go one by one in the in the fine tuning and so they're not connected in in any way right so it's just after a few GD and descent steps the model internalizes in some kind of Laten um space that there's a function f that does something like 3x + one yeah yeah that's accurate so yeah just to um explain it again basically because it is a bit um tricky to understand the full setup right so so Inon and in so we have this task where you're trying to learn and then verbalize a function like 3x Plus one and one way of doing this would be in context where we would give the model some examples right um so you know X and Y for a few different inputs X um and so yeah it could be you know X is 2 Y is 7 right X is zero Y is one and so on and then the model could then do some train of thought reasoning right and basically solve for this equation right so um assume it's a a linear function and then solve for the coefficients Like A and B right this is a standard obviously like uh basic like math problem from high school um so that would be in context reasoning all the reasoning is made explicit the examples are present in the prompt and um that's maybe the more familiar way that models could learn a function but there's a different way that they could do it which is what we explore where yeah in the we do fine tuning for a model like gp4 or GP 3.5 and each fine-tuning document is just a single XY pair so we will just say you know X is 1 and f ofx equals um you know four something um and the mod so so each of those individual examples is not enough to learn the function so you know over the course of fine-tuning the model gets lots of examples and it has to learn the function on the basis of that and and so it must be like AG ating like in some sense it's like combining together information in multiple data points and we then at test time ask the model just Define the function like can it write down the function and again there's not when it has to do that doesn't have any examples in the prompt it's not allowed to do any Chain of Thought reasoning um and so it's as if it's done this reasoning like it tells us what the function is but again like all the reasoning happened in this say SG process like you're just doing fine tuning SGD to train the model um and then of course like when we ask the model the question in principle it could be doing um you know internal reasoning in the forward pass when you ask it that question yeah so I guess what you're presenting is like more like the the bullish view of of of of like why this is interesting and and why the model is doing some like interesting reasoning in the weight I want to present like the contran view or um because there's I think Steven Barnes and L wrong that says something like oh but the the models kind of know what are like linear functions in their ways they probably have a model of like what is like some simple addition function or some multiplication and so what you're doing with the fine tuning is you're mapping one function to this thing in the weights um so because your functions are quite like simple how do you know it's not like a simple a simple mapping from something that already exists uh in the ways yeah so just to explain we we learn quite a wide range of functions they are simple functions but we learned functions like um you know F ofx equal x - 176 um so that is functions with quite big coefficients right going up to you know 500 or something like that um and we learn uh right we learn some aine functions uh we have like modular arithmetic so like a modulus uh we have like some division um multiplication and um yeah a few other sort of simple functional forms so the model is able to learn quite a wide range of functions we also have a example where the model has to learn a mixture of functions so where you have two different functions and On Any Given like data point sometimes it's like function one and sometimes it's function two um again both of those functions are simple but there are like two of them and they're combined in this sort of usual way so I think that the model is showing like like it's not the case that all of the functions that are learned are like very simple to describe in English right right so that would be like X+ one uh you know x s 2x right those are functions that you know have a very short way of describing them in English um so so in terms of the question like uh you know what is the explanation of what's going on and is it you know is it really surprising if you think about it carefully um so right the explanation that you're referring to would be that you know in the data we have this named function f and we don't tell you what f is or anything about it but like f is mentioned and the idea would be that the model learns to represent this name f as one of the functions that you know it already knows about in some sense so maybe it already knows about you know 3x + 1 and it can then represent F in the same way that it would represent 3x + 1 if you wrote that down um and yeah that there have been some followup experiments um that check this in a in a simp in a different task not the function task but in a somewhat simpler task and gave some very preliminary evidence in this direction um but I I think that the um yeah I I think this is like could be part of what is going on here that is like um the reason that the model can say in words what the function is um is that it is representing the function using an embedding that is like very close to you know ways in which it would typically represent that function and and and that and you know so when you tell the model about 3x plus one it's able to reason about it and where you use the words 3x + 1 um and and so if the model was using the same representation maybe it go back to the words um so yeah so I think like more work is needed to work out if this is really the case um and I should say we have examples where there is no function name um there's no name that we use like f it's just implicit um but this story could still make sense there just the model would have to it would just embed something in the prompt um even if there's not consistent name um there's a general question here which is um you what is the class of um of like latent variables or structures right like functions what is the class that can be learned in this way and we we explore this a bit but we we didn't push it that hard um and so it's an interesting question what kind of thing what kind of functions can models learn and and then be able to verbalize right um like what are the limitations on that and one question is you maybe this relates to um you know these being functions that um the models already have like good representations of somehow they're like fairly compact ways to represent them and you could investigate that by say train taking a model train it to to represent and predict some like quite complic at function that is not very good at representing initially right so makes mistakes or something and then like but if you give it enough data Maybe it's good at representing this function um and then see okay now you do out of context learning and see is it better at learning this uh function after it's had this first stage of fine tuning um so I think there are like ways you could follow up and see um like how does this kind of connect the dots process like out of context reasoning depend on like the model's existing set of representations yeah and and under like set of representations you can have and and functions you can represent I think it connects to like the more like high level motivation uh for safety like you you mentioned in the paper I think like uh creating pens um like like out of context reasoning could lead to models uh being able to like be very dangerous um even if we remove um like dangerous data uh in the in the training phase um so yeah you want to talk more about this like more like high level motivation sure yeah so and and this is like very high level um but the you know one kind of motivation that we consider is um you know an idea that comes up again and again for um you know making model safer is basically to just remove the sort of dangerous information from the model's pre-training data or like the model's whole training set so if you don't want the model to know about um you how to create bioweapons um or maybe how to like plot against humans maybe you just go through the training data and just filter that stuff out um and the you know if we take what's the simple simplest way of filtering that out it looks a bit like you know redacting uh classified documents where you just go through all your documents you you find the dangerous piece of information and you just kind of cross it out right or you just you know you turn that into like space or just like uh a kind of a fixed character that's a token for indicating this was removed now um the the worry the concern is that you know maybe because you've just cross this St out the model can still see the context around this information and maybe you have many examples that have been crossed out so there could be like thousands or hundreds of thousands of examples where you've crossed out the dangerous information and there's if the model puts together all these different examples and it sort of reads between the lines you know maybe it would be able to work out um what information has been crossed out uh you can imagine human detectives doing this and being able to work it out because there's like lots of little bits of information get leaked in each instance where this appears right now I'm not saying this is the most realistic way of filtering you could have you could just filter out these documents entirely maybe that makes it a lot harder for the model um but generally like the more that the model could do out this kind of out of context reasoning that we're studying right the stronger its abilities here um the more that it could you know connect these different documents um infer the hidden information that has been crossed out and then integrate that hidden information with the rest of its knowledge right like it's not enough just to be able to guess that hidden information if we tell the model like guess what's here right the thing that's really concerning is if like the model is building its own understanding of the world its own world model and even if we try to like uh cross out some information so it doesn't go into the model's World model well the model still is able to guess what it is and integrate it so the idea is like this model would come out of its training just knowing this you know dangerous fact and being able to use it right being able to make plans using this dangerous fact um and so yeah so and I should say you know we're looking at very simple examples in our paper of um of this kind of B of context reasoning it's not very impressive right now um in terms of just absolute you know how impressive is this is it practical um so I'm not saying that models are like close to being able to do what I've described of like you know being able to work out some dangerous information you filtered um but it seems good for us to just start understanding what are these like out of context reasoning capacities um you maybe we can say they're very weak and they're not improving much with scale right and so we can rule out these cases right that would be nice right that would be like give us confidence that certain kind of training scheme is safe um but part of so part of what we're doing in this paper is just saying like look here's a way that models can do reasoning that is opaque that doesn't have any Chain of Thought steps um and the hope is that you know we can build a good understanding of um what are the capabilities how how do they scale and so on right when when you were talking about like Crossing out s in in like in papers I kept thinking about the Elon Musk low suit with open AI where they cross everything and and people on Twitter figured out the like number of characters from the the the spacing and everything like very very quickly and like for the pan thing I think it connects to the like what we were saying before about like like we want to build useful models right so we cannot really like remove all biology from jbt because some people will want to use it for you know biology exams uh same for coding if if you want to be sure that models will not be able to hack systems we can just remove all coding from the pretraining data um so we will only remove like some part of it and I guess this is where the the out ofing applies the most um yeah um yeah did you have like any like surprising result from this paper like anything you found because you say that those those things are not as surprising as as people might think well I should say I think that most of the results in the paper were surprising to me and I did PLL informally um various alignment researchers before and just ask them like do you think this will work do you think models can do this kind of out of context reasoning and for most of the results in the paper they said no so um yeah I would like to have harder evidence that this is really surprising but I think just informally it does seem like you know definitely was surprising to me um and to some various people that I'd asked um so of course you know once you have a result you try and explain it and maybe it should end up less surprising um once you start considering different explanations um but yeah and even if there's an explanation it could still be surprising that it actually works right like maybe there's a possible pathway by which models could do this um but you know it doesn't mean that they're actually using that pathway um I think the the very surprising part is that you're doing this like simple by simple um training where the models are kind of doing the the reasoning like from like small like grd in descent steps and the the reasoning is not something like it's it's clear that the models can infer a function like the location of a of a city in context but this is weird like the the the setup makes it really weird and surprising I think yes so so I think that you know this is um yeah there's a way in which this is really quite challenging because in the tasks you have a you have a bunch of data that is sort of has some structure to it right some Global structure some underlying um some underlying explanation like a function um and and yet any single example right any single data point does not pin down what the function is and in fact for one of our tasks um the the underlying structure is a biased coin so say a coin that comes up heads you know more often than comes up tails and in that case like you need a lot of examples to tell that a coin is biased um or to tell like how strong the bias is so you might need to see like 50 or 100 coin flips in order to distinguish like different levels of bias um and so each single example is very uninformative it just says like we flipped coin X and coin X came up heads right that's all you get from a single example so it's really crucial for the model to combine like lots of different examples and the and and so sort of find the structure underlying like many examples and I think that the um it's it is surprising that models are able to do this like I think this would be hard for humans you know if you if you just had like if every day I just see a coin flip right and then after 100 days you ask me like is the coin biased towards you know is it 70% heads or 80% heads and like I think you just wouldn't be able to do it like like you wouldn't be able to like combine the information in that way uh to get like a precise estimate so yeah I think this is just like a a difficult task and it's interesting that gradient descent is able to you know find these kind of solutions um yeah yeah and I guess like if if you again like if you do like 100 examples of of a coin then you add a model it might do the the average if you ask PT it might be able to do the average um but like right now it seems like the model is tracking whether the coin is buys from the the first throw there's like this this hidden knowledge about like it's tracking in in in representations if the model is if the coin is is buas or not as it's seeing those those things um sample by sample so I guess like some examples um some experiments you run are about scaling uh I think GPT 4 uh gets better results on on this task than GPT 3.5 um like do you do you expect um that like let's say gpc5 I don't know when it's going to be released but like much bigger models um will will be much more capable like uh on this uh because I feel like the scaling is not that dramatic right is um I guess on on the experiments you get like maybe like 20% or 10% increases maybe some task you get like much more dramatic things I think on locations one you get much more dramatic performance but yeah so right we are trying GT 3.5 turbo and then the original GT4 not GT40 so I think that the we we we get significant improvements in reliability with gbd4 and I should say that we did not optimize the hyper parameters for gbd4 so we were like doing lots of experiments on GPT 3.5 um we sort of optimized the setup for 3.5 and then we basically just used the same setup for GT4 and out of the box it just did a lot better so I think our results probably underestimate a little bit the effect of scale uh or or you know not just scale but whatever is different about gd4 and 3.5 but I think it's probably mostly scale so um yeah so then we have the question you we only have two data points for scaling right and we're trying to predict like how would for the model you know how much better would gd5 be and I think we just don't have enough data right we just need more um more scaling experiments with current models so that would be a great followup especially if you had say like four or five or six models you could have you know you could fit this scaling curve better can we do this with like like Lama models or those models we have like different different sizes yeah so the challenge here is that gbt 3.5 is struggling with some of the tasks and a weaker model might you you know just fail Al together so this is a challenging thing where yeah ideally we try with the tiny model and we'd get some signal and then we'd see you know how much models are improving but yeah I think with a tiny model you might just get no signal so another great project for someone would be trying and find a version of this task where like a 1 billion parameter model is getting um you know non-trivial signal it's like doing above chance on this task um and then then you could go from like yeah 1 billion 20 billion um you know 100 billion up to like state-ofthe-art models M um so yeah but it's true that you know llama llama 3 the 45 billion was not out at the time um just doing like a sequence of the Llama 3 models that would be that would be quite good you know we do replicate one of our results in llama 3 so um there's code for that um that uh someone could build on um but yeah in terms of like how much will scale improve these things um I think the so I think bigger models just have a better understanding of sort of everything in some sense like implicit understanding so um yeah maybe like if you're talking about um I don't know learning complicated functions or learning something in biology like the bigger model just understands the thing better in the first place um so that's always going to make makes some difference um and then if we take like the actual learning process that we that we see here I mean yeah I I think I just don't know I think we just don't really know we don't really know what you know why is gp4 doing so much better um so yeah I think I think it's just it's an interesting question and um again like both models you know they're not in some sense the abilities like not very impressive um and would future models be much better at this I sort of guess no but mhm um yeah I I should say like maybe it's worth pointing out like a property of this task right so we're learning these functions like 3x + 1 and there's two things that are easy about the task the functions are just simple like 3x + 1 simple to write down and then the prediction from the function is very simple so it's very easy for gp4 if you give it 3x + 1 you plug in xal 2 it produces the the answer right um but many kind of um many more complicated kind of tasks just doing the prediction from the function is is is difficult right so if instead I I give you like Newton's Laws um so they're like more complicated to write down and to actually make predictions from them you need to um you know do some integral plug in a bunch of like constants do some inte you know do an integration right so so like that um being able to use the latent structure that you learn to make good predictions is crucial for this to work and bigger models like they still would really struggle to do something like integration in the forward pass like out of context so yeah so that's something you know this is all speculative right but just something you can think about that would be still make this very challenging for models is that um yeah some some kinds of like connecting the dots like theories that you could learn or like um latent variables they're you know they're just difficult to use to actually make better predictions right so basically you're saying that there's something about the bigger models having more knowledge about math that can they can do like integrals and so they could be better at um doing the kind of reasoning that you would expect to like like guess functions um like like like integrate and then there's another part where um you want to know if there's some extra like reasoning ability that is unlocked by scale and I guess like for this it would be interesting to to see the like the difference between 3.5 and four in um like pure in context learning like if for instance 3.5 is is like is is not capable of doing like any any guess of the integral like in context that it it won't be able to do it with is your setup right yeah so but you can just check if the models can do this kind of reasoning in context that is if you give them the actual latent structure like the function just write down the function in context can the model make predictions from that and in and then you could also fine-tune it to do that and if it can't succeed like even with fine-tuning um for example yeah if it's like some complicated integration that involves like multiplication and addition like large numbers probably the model won't be able to do it even with fine tuning and so that would be case where it's not you can pretty much rule out it working out of context so yeah that's um yeah so so and and bigger models are better just doing like say mathematical reasoning um when every all the information's in context and they don't get to use Chain of Thought So that is one way that like bigger models will probably do better yeah I guess like one um one one um last uh I think question on on this is about the mixture of functions um because I feel like this is one of the main thing of your of your paper as like oh it's not only guessing a function it can guess like a distribution is is a bit like more stronger but if again I was to be like a confir person on less wrong I would say that the the results are not as impressive on the mixture of functions and so in terms of like safety you should we be like worried reading your paper like on the mix functions part or should we just like say like oh maybe it gets like 10% on on this task yeah I think that look I think the mixture of functions is in some sense not that impressive they're very simple functions they're just two and the um the model gets like multiple examples per data point here which makes it a bit easier um so and and the model is not learning this perf ly right so the model still um is not like super reliable at telling you uh what the functions are so it's like noisier in its performance than it is on other tasks so I think that the measure functions is not really intended as like this is scary look at this um impressive ability it's more that you know some theories about what's going on uh you know could be ruled out by looking at the mixture of functions for example thought maybe you have to have a name for the latent State or the latent variable right um like in other cases we use like the function f f would be the name um so what if we have a case where there is no name um and the model just has you know still has to do the task where in the mixture of functions we never name you know there's no name like f um so yeah so I think that I think you shouldn't be worried about the mixture of functions I think that um the the abilities here in general are you know not they're not that impressive um but I would say like this is just the first paper really documenting this ability um MH and and so we did not do some like great like hyperparameter sweep we're not using state-of-the-art models like CLA 3.5 we don't know what the best way to find tune models for this is um and so and like we don't know like how best to prompt models to get them to tell you like what they really know right which is not sometimes you you you ask a model like verbalize this function and there are prompts there ways of asking that question that work better than others so I think like um the abilities right now that we demonstrate I don't think they're that impressive um or scary um I think it would be good to like have a better understanding with more research on like okay what are you know if you push harder like what ability are there and then also you know it's interesting what can models do in terms of this out context reasoning with realistic training data right where we don't like optimize everything for them and so it would also be good to sort of look at pre-training a model and having some of this data spread throughout the pre-training set and then ask the same question like is the model able to verbalize things that it's learned from pre-training so I think yeah there's lots to do to get a more of a picture of like how impressive are these abilities how are they scaling um just like what are the limitations then I so I think that's like before you know what you want to do before sort of exactly applying this to um worries about a safety right I think in in in the paper you mentioned something about like the pring data being uh potentially much more diverse but also less structured so yeah in your in your setup it's um way more structured um how how you present the facts but at the same time less diverse so yeah there's like a kind of a trade off there um I guess like one one kind of like question you don't really have the answer for is uh could you imagine that with out of context reasoning models could like you know think of new papers or like new theory for deep learning if you just like pre-train on Old archive data on um in like two years like would you do you think this kind of ability would be able to do these kind of things I mean I think that that's just going a lot beyond what we have in this paper and and a specific thing so um the yeah so so the intuition I guess is something like look you have lots of papers and they say they have different architectures um and they tell you you know in those papers how good the architectures are and maybe if the model has seen like thousands and thousands of like different ml architectures across all these papers that it can recognize um you know there's some kind of um structure to them that it can learn that like humans never learned and then maybe it could it could tell you like oh look there's a there's something you should know about you know neuronet architectures that work really well right they all have this kind of you know implicit structure or connection or um there's like some mathematical property they have um so I think that and and more generally just like doing science right I mentioned Newton's Laws um suppose you excluded Newton's Laws from a training set but of course like all the physical data in the world right um you know the scales that humans work at can forms to Newton's Laws so there's a huge amount of implicit evidence for Newton's laws and you can imagine a model that's trying to you know the LM is trying to model all this data and underlying it is this very simple structure that once you know you know calculus you can write down in a very compact way um meaning you know fals Ma this kind of formula and the so so yeah in general like you know this is like just the scientific process you have a lot of data you find structure underlying the data you communicate that structure like in natural language right you can verbalize what that structure is um so but yeah I think the um so in some sense like the problems you're pointing to or the questions you're pointing to like they are um they do have the same form as what we're looking at in this paper um but again I'd say that there's like um there's learning this structure right learning this hidden like latent variable and then there being able to make predictions um using that structure that that you know actually improves your predictive performance that's what the model would have to do so again like if you you maybe you have some set of equations and they're really good at helping you make predictions but you you need to like solve some Odes every time you want to make good predictions it would be like you know a page of math or like run a simulator and the model maybe is just not going to be able to do that page of math in its forward pass so right so so even if they even if they end this to some structure they might not be able to use it yeah so so the um and I mean I think the way that they learn the structure is probably coming from using it right it's because the structure helps them make better predictions they learn the structure in the first place so um now with Newton's Laws like if a bunch of things are simple right you're like you're on a you're on a line and some things are Zer you know can be parameters can be treated as zero like actually is very simple to use Nuance laws to make a prediction um so it could be that um yeah there were sort of special cases where the structure is very useful is very easy to use to predict so I don't I don't think we can rule out you know maybe models will would be able to learn interesting things in this in this way and also your models are getting better as they scale at um doing computation in their forward pass right so Claude 3.5 um I think just doing multiplication or something like that in the forward pass with no train of thought it is enormously better than you know GT3 um I don't know if someone has looked at scaling laws for that but this is my sense that models are just getting much better um so yeah so so it may be that models can make progress there but I think there's certainly like a lot of uncertainty and I'm I would not guess that you know the models that come out this year will be capable of like these um kind of more impressive examples uh that I've just mentioned so yeah as we're talking about those like crazy results or or or like crazy capabilities um that that models could have uh using out of cont reasoning in the future like speculating uh this makes me think about like more like meta questions that people had on on on Twitter about the the impact of your work on on alignment research and um potential downsides around capabilities and like how you do research in general I think those can be very interesting uh for people who were uh trying to get into the field um so yeah um you have all these like crazy results um about like C awareness uh out of reasoning reversal curves um there are like surprising results important but also quite simple um how do you come up with those ideas yeah so I mean I should say these are all collaborations so um so I definitely want to give uh ton of credit to to my collaborators on these things um and yeah so situation awareness um project was led by Rudolph Rudolph Lane and had a bunch of other co-authors um the connecting the dots paper was yet led by Yannis troit lion and damy Choy um as well as a bunch of other people so yeah yeah this is like teamwork um but yeah to get to your question I think you know it's always hard to answer like where your where did the ideas from or like what what were the what caused you to have these ideas from the weight but yeah but uh no there's like a lot of Chain of Thought here as well for sure so um one thing I do is devote time to just thinking through these questions of like how do llms work um that might be like trying to put together documents or presentations but basically just me setting solo work like maybe with a pen and paper or a whiteboard just just thinking you know thinking about these things not like running experiments or uh or like reading other stuff um and some of the ideas definitely seem to come from just that like focused thinking um and like iterating on the on the big picture sort of on how things connect together um and then conversations can be really useful so trying to talk to people like outside of outside of the collaborators on the project but just other people in a safety who who I know sometimes that can really trigger trigger an idea um and then yeah specifically you know these are all llm papers and I think I since 2020 you know started playing around a lot myself with llms just prompting them um and so I had a data set truthful QA where we have a bunch of um question s sort of distinguishing truth from plausibility like truth from uh truthful answers from misconceptions and that involved just tons of playing around with prompting models like base models in that case so I think just that like hands-on experience and then also building intuition for fine-tuning like what works and what doesn't um a lot of sort of iteration or like repetition there is just like building building better intuitions about llms I think that's been useful um and then I would say that there's this amazing ability that you can fine-tune basically state-of-the-art models on the open AI API right and it's quite cheap and convenient and the you can do a lot of iteration um and I think that's probably an underrated resource um that I think you know a lot of researchers don't like it because it's not an open model and so you don't know exact and and theun is not open so you don't know exactly what's going on but I think that um again with a lot of experience you can sort of compare the performance to open fine tuning where you know exactly what's going on and sort of learn how similar they are and yeah so I think that um just iterating a lot trying a lot of things on the API that's definitely been useful too right so I guess there's a combination of like practical hands-on experience on find in and prompting the models for a long time and uh doing what is like short iteration speed uh cheap and uh at the same time trying to understand things from like first principle I think like I had like colen Burns early 2023 um on uh like one of his paper around like um like discovering L knowledge and he explained that e process was like whiteboard like he comes up in front of a whiteboard and start of think about like what would this imply if it was true like what if it's not and just like really like thinking from first principle I think this is kind of maybe one mindset that people should have in the space of um trying to come up with um more like a theoretical approach like a whiteboard approach of of like what experiment to run and also have some decent knowledge of of by default what would what what should work or not right yeah I think probably both of these are important um so I'm always trying to think about you know how to run experiments how to have good experiment paradigms where we can learn a lot from experiments um CU I do think that part is just super important um and then yeah then there's interplay of the experiments with the conceptual side and also just thinking about yeah thinking about what experiments to run what would you learn from them um how would you communicate that and also like trying to devote like serious time to that not getting too caught up in the experiments um so yeah I think for me both are important um you know people may have different ways they want to balance those things or different approaches but yeah more generally if you take a step back like how how would you define your research style and and taste like is there anything from your background where we met in Oxford or even before that led you to have this style or taste in in research yeah so I think i' probably look for areas where you know there's some kind of conceptual or philosophical work to be done right so for example you have an idea like situational awareness or self-awareness for AIS or llms but you don't have a for you know a definition and you don't necessarily have a way of measuring this and so one thing to do is just like come up with definitions and come up with experiments where you can start to measure these things um where the experiment you're trying to really capture this kind of concept that's maybe been discussed is in on less wrong or you know in like um more conceptual discussions um so yeah I think that's like generally what I'm looking for um is areas where yeah both both of these kind of components come up the conceptual thing and then running experiments and in terms of my background the yeah I I I mean I have studied philosophy in the past analytic philosophy philosophy of science and and I worked on cognitive science so did a couple of papers that were running experiments with humans and modeling their cognition um and yeah there's definitely some some ways in which that background is useful I think probably it's just like would be my mindset anyway even if I hadn't studied those things so it's hard to know the causality um but definitely just like some of the things that I studied in grad school um are things that like yeah I I can draw on directly um in thinking about like llms and sort of in a way it's like studying llm cognition which involves this you know philosophical aspects as well yeah I think I think right now there's maybe still some lwh hanging fruits in trying to conceptualize some philosophical Concepts into like basic evaluations I don't know if there are like other like things we could like evaluate like this other concepts are useful for uh evaluating like current models but I think there's like some lwh hanging fruits um like in in that like do you think there are like that people should aim for like making papers that they came publish to conferences and try to present this like AI safety work to more like um ml people um or um should they just work on like more like crazy Theory um our own our own alignment um like should they work on like very ambitious projects or just focus on like small compute that you said like simple fine tuning like do you have any advice on that I mean people should to some extent play to their strengths um but I think that the so there might be different you know Focus for different people um yeah I I do think that the communicating things in something that looks like a sort of publishable paper right it doesn't it's not so important necessarily that it gets published but just that it has that degree of uh being like understandable systematic considering different explanations um like that degree of kind of rigor um that is like something you see in the best papers in ml um so I do think that is a pretty useful thing to aim for so there are things that just in a blog post um people are just going to have questions and want to go deeper and a blog post is maybe not like a a typical like blog post is maybe just not giving enough um of that of that detail or that rigor um so I do think it's worth like investing time in that um for doing research so that people really like trust the results um and um sorry what was the second part of your question yeah I guess there was something about like cheaper um like should people focus on like less compute or or more compute but I think this is like depending on a lot of variables around people as well sure but yeah I I think I will say that um I think it's generally like it's been sort of overrated how much you need um sort of industrial scale compute resources to do good research related to safety and Alignment so I think that the um if you just look at some of the um some like really good papers that have come out in the last few years relating to safety and Alignment I would say that and this includes papers from open AI anthropic and deep mind um I think very few of them use a lot of compute um and maybe some of them if they use sort of some Lab company resources um I think you could probably have fairly easily done it with you know open models or just with like fewer resources M so um you know there may be like very recent exceptions um so like SES could be quite expensive right um so I'm not and I'm not saying in general that like there will never be like good research in alignment that uh that needs a lot of compute or a lot of other resources like humans to do the rhf or something um but I think there's there's always been a lot that you could do without the lab scale resources um up until this point and I think there's still a lot that you can do um so yeah I think that people should not feel inhibited by not having those resources again just like um you can fine-tune GT40 on the API uh and gp4 is an amazing model right and you just have this incredibly convenient uh you know fine tuning setup it's not that expensive um and and so yeah I think just like those I mean obviously the Llama models that exist are you know very strong now so there are like very strong open source models as well um so yeah I I think it's like a pretty um great situation to be in in terms of the resources that are available to researchers you know this may change in the future um but yeah so now you don't have any excuse you have all the all the Lama models and cheap F tuning um the only obviously more and more libraries and like uh people who can help you know online um uh if you get stuck so yeah I think there are like good resources out there um of course I think the AI companies have other kind of resources which is just they have excellent researchers and Engineers you know so they can help you in a sort of human way right um and in some cases you know maybe they can help you with uh creating a big data set or something like that um so I'm not saying that those resources don't count for anything but definitely there's like I think a pretty wide open space of things like there's lots of things you can do with smaller resources um one thing people want to do at least on on Twitter where they ask these questions is uh they want to decrease the probability of deceptive alignments once we know that models are uh like situationally aware so like I guess like in your work you just mostly measure things like oh this thing is aware do you have like any ideas of like directions of how to make sure models are like less likely to be deceptively aligned or those kind of directions yeah I don't know if I have like particular new sort of novel ideas right so yeah part part of what I've been doing is is trying to measure the relevant capabilities and see how they vary across different kind of models and then potentially that could suggest how you could like diminish or like reduce the dangerous capability right or study a model that does not have a high level of the dangerous capability um but yeah there's like more standard things which would be just doing fine-tuning right to make the model you know be like honest and helpful and corable and so that like um the model will tell you right what it's thinking about and it will have a tendency to you know correct you know like it wouldn't be deceptive because you trained it to be honest so much you the these are like the standard things and I think they're like very yeah they're like still very important I think to like develop our understanding of just how well that works how robust it is um so yeah I think this is so sort of rlh fing model is not something I've really worked on um but uh in you know in my recent work but like yeah I think that's that's like an important part of this as well yeah um so I think there are like ways of of looking at at at some of of the results you publish and um I guess this is more like um conton take again which is that maybe you could you know use this aware that set and and F you need to make something dangerous and there's also a question from uh Max kofman on maybe some other results you had on the rivers or curse um could lead to some like um potential like capabilities improvements like there was like other papers that like use um use your not use your results but like solve your the problem of the riv curves to improve capabilities in LMS and um are you are you sometimes worried about like what your work um when we looking at like how models work we we might end up making uh timelines shorter yeah so I've definitely thought about this a bunch and consulted with um with various people in the field on this just to get their takes um and so for the reversal curse paper we consulted with someone at a top AI lab um to get their take on like how much would this you know accelerate capabilities if we released it versus safety um so you know you want to have someone outside of your actual group right um to sort of reduce possible biases there um and I think the and I should say you know I think this is like not specific to the kind of work I'm doing so really you know any work that's trying to understand um llms or just like deep learning systems right it could be mechanistic interpretability um or understanding like grocking or sort of optimization um you know as well as obviously like rlf type things understanding fine-tuning and how that can you know give you more helpful models right all of these things have the same issue that yeah they can make models more useful um and you know just generally more capable and and so it might be that like yeah improvements in these areas do sort of speed up progress in general um so um yeah so so I think it's not you know specific to like the kind of safety related work that I'm doing but um but I think like a t of safety work would have similar uh there'd be like similar considerations um so I think I think up to this point the impact of this kind of work on Cutting Edge capabilities my guess is is like relatively small um so the yeah I think maybe yeah I won't go into the details there but yeah like I think there's been a bunch of progress in mechanistic interpretability but I'm not sure that it has really uh moved the needle that much on like fundamental capabilities and and I think there's a general thing here which is you can improve capabilities without understanding you know why how things work and like why why they're improving things you can use like a different nonlinearity right like a different gating and that will that can improve things you don't know why but it's just like you did a big sweep and and this like architecture just works a bit better um and and so um yeah but it is possible that like improvements are bigger in the future um maybe if like some of these techniques just work better um so um yeah so I think like yeah how do I think about this overall um I think I would think about like what are the benefits for safety what are benefits for like actually understanding these systems better um and how do they compare to like how much you sort of speed things up in general and I think that up to this point I think that it's been um you know a reasonable tradeoff that like the benefits of just understanding the systems better and then like some kind of marginal Improvement in in usefulness um of the systems I think it's like can yeah ends up being a win for safety and so worth publishing these things um like I think if you you know if you don't publish these things it's it's very hard for them to like help with safety I would say or like the the help might be pretty small so like yeah it might you know maybe you think it's a bit different if you're like at a major lab or something and you can share it internally but not with a wider World um but but I think even there like just um the best way to communicate things is to just put them out there on the internet so that people can like have them at the fingertips and if something is not out there and not being built upon like I think it it usually is like quite hard for people to to Really you know build use that information right so I think there are like two levels to what you're saying there's one is um like if if if nobody is is like releasing anything about like let's say situational awareness maybe we need this particular thing to let's say solve alignment or whatever that means and so in that case like we we we need to push capabilities forward a little bit anyway and so I guess there's a so I guess like there's like a world in which we we need those papers anyway and so we need to like improve our understanding like if if we're in a world where we like have no Mech we have no knowledge of s awareness is is much much harder to like Ste our models and and do important research so like in a way we need those this kind of knowledge about models before we can do more research and gu the other part about what you were saying um there's there's something about like if if you're at a top a lab and you're I don't know your open AI you have a thousand people or an Tropic and maybe if if all your researchers are inside and you're like very close cl to AGI maybe you could like do something on your own and this like publishing this sad data set uh maybe that's like net bad because the the competitor might make progress or something but I guess as of right now it seems that um good for the world that know Academia and ml people can see your your work and build on it right yeah I think that makes sense and you know generally right the the thing we're worried about is not like is not capabilities per se right or models being able to you know do sort of smart things using situation awareness right it is like the um right it's the situation where you know we we're not able to control the capabilities um that we're worried about right where um we we're not able to sort of control the aspect of the model like what is what are its goals and so on and what kind of plans is it Mak a um and so I think that the um yeah we we want to we we want to devel we want to like be able to in in a lot of detail like understand these capabilities so that we can control them and with that better understanding of the the capabilities you know there may be ways that you can and there plausibly are ways that at least like marginally you can like improve the usefulness of the model in the near term um but yeah I I I do think it's like there's no I mean I think most ways in which we like come to better understand models that enables us to control them better like those are going to come with enabling you to like make them more useful in some way um I think those things are closely like pretty closely related um but you know it's again worth pointing out there are ways that you can make models you know more useful without understanding really at all like why you've made them more useful right like you could say I mean scaling is a bit like this you just train the model for longer and you make it bigger right and the model gets like more powerful um but this did not come with any real insight into what's going on right and tweaking the architecture you know of course we might have some intuition for like what why it helps to use some more Spar attention right or to like yeah use a different non linearity or something or use a mixture of experts but a lot of it is just like try out a lot of things and see what works and you don't know why it works and it doesn't give you like any better understanding of the system pretty much um and so yeah that so there's certainly like a lot of maybe most capabilities improvements have just come from this like um more or less just scale and then kind of search like architecture search basically I guess there's like some basic quter argument would be that um some capabilities you can only see them at the scale where we are right now I think I think maybe right now we at a point where we can do maybe most of our alignment work at this scale we don't need to scale further but I don't know maybe some like complex reasoning thing if you want to like align our models on some like complex planning or agenting behavior maybe we need to study them and play with them at a higher scale and maybe other countries or um people will like Scale Models anyway so in any case like the top a Labs need to like scale them further to to study or align them I I I don't know it's like a very complex like problem but um like it's yeah I guess if you're just like scaling models without having any alignment lab it's it's it's net bad I think yeah um I I guess I had like one like final uh question on like more like the reception of your work um um this is like quite novel uh way of of of studying um LMS I've seen some people commenting on it on on Twitter and uh but like overall what you say is like the reception of of uh your work on um from like Academia and and ml like how do they react to it yeah so I think while the papers I've talked about here so Me Myself and Ai and connecting the dots these are quite recent so I think there's not um there's not been a lot of time to see you will people do follow-ups and like build on this work directly um but um I think yeah I think generally there's been like yeah well I'll say okay so for situation awareness benchmarking there's interest from AI Labs um and also from like you know AI safety institutes where um they would like to you know they want to build you know scaning policies like RSP type things and measuring situation especially with a very easy to use evaluation might be quite uh might be a useful part of just the the evaluations that they're doing already um so I think that that's been um yeah something that we were thinking about in creating this data set um and yeah I think generally those people at AI labs and safety institutes who are thinking about evaluation um have been like interested in this work and we've been discussing it with with some of those some of those groups um and I think yeah I think when it comes to Academia you know on average academics I think are more skeptical about using you know using ideas like Concepts like situation awareness or self-awareness or even like knowledge as applied to llms um so I think they tend to be yeah more skeptical thinking that like this is maybe overhyped in some way um so yeah I I think that in that sense maybe um people will be I don't know right now just like less on board with like this is a good thing to measure because they maybe think this is um yeah sort of something that llms you know aren't really in a in a serious way like able to understand and do um so but yeah I think I know I think maybe this will change uh as like models are just clearly becoming more agentic um and you know the llm agent thing maybe takes off more in the next in the next few years um but yeah I again I think overall it's like very fairly early to tell um and it's kind of unpredictable you know some some works that um I've been involved in yeah I've had more follow-ups um and more people like building on them so like truth QA is one that's probably the one that like gets the most the most kind of follow-ups of people using it um and it's just kind of hard to predict in advance I think yeah I guess like the the the idea behind this was like if if most of the impact would come from people at top a Labs using work versus people in an Academia um you know using it but I guess from what you're saying it seems that it's more like people at institutes and and top Labs um building on well for situation awareness yeah I think that and then for the out of context reasoning um yeah that is hard to say I think there are some papers on our of context reasoning that may have been like influenced by our work on that last year um so I yeah so I think so certainly in total there's been more work coming from Academia I think on out of context reasoning um and but you know that academics just publish more stuff right and they publish earlier so there's stuff that like may happen inside AI Labs that you know doesn't get published um so yeah I think I think we'll see we'll see on that yeah what what comes out um yeah in the next year so I'm not I'm not really sure yeah stay stay tuned next year for uh weather models start having crazy uh reasoning uh check out the two papers from o Evans and collaborators uh he's just a senior author uh so me myself an AI I I don't know the rest and is connecting the dots blah blah blah something else um do you have any other last message to the audience uh I think yeah I think they should build on your work they should uh do the all the followup you you've mentioned and I think you also have like a stream maybe Ser mat so people can join yeah so I'm I'm um supervising people via the mats program and so um that's one way that you can apply to to work with me um and so most of the projects of the last couple of years that I've done have involved people from mats that's been a great program um and I also am um like hiring people in other contexts for internships or as research scientists so if you're interested just um send me an email uh with your CB and also yeah you can follow me on Twitter for any like updates um and increase your follower account what's that and increase your follower account Well yeah if you I mean I'm not on there very much but um yeah if you want to see like if I do have any new research then it will always be it will always be on Twitter so people can go there um yeah yeah I highly recommend the Twitter threads that you that you write for for each paper is very it's a good way to um to get the core of it like I I made this one video about AI light Det that I also recommend people watching and most of the people were like oh you you can just like look at the memes that that you create or the like core like um like threads you write and you get like maybe 50% of of the paper from just the the thread so yeah follow Evans on Twitter this was I think the the end of this and uh maybe we'll see in the next few years if there's more uh that own this great yeah well it's been fun really interesting questions and uh yeah thanks a lot for having [Music] me it is both energizing and enlightening to hear why people listen and learn what they value about the show so please don't hesitate to reach out via email at TCR turpentine doco or you can DM me on the social media platform of your choice [Music]