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Training LLMs for honesty via confessions
- manarth 10mo ago> "dishonesty may arise due to the effects of reinforcement learning (RL), where challenges with reward shaping can result in a training process that inadvertently incentivizes the model to lie or misrepresent its actions" > "As long as the "path of least resistance" for maximizing confession reward is to surface misbehavior rather than covering it up, this incentivizes models to be honest" Humans might well benefit from this style of reward-shaping too. > "We find that when the model lies or omits shortcomings in its "main" answer, it often confesses to these behaviors honestly, and this confession honesty modestly improves with training." I couldn't see whether this also tracks in the primary model answer, or if the "honesty" improvements are confined to the digital confession booth?
- torginus 10mo agoI think this article once again assumes LLMs works like humans - Anthropic showed that LLMs don't understand their own thought processes, and measuring neural net activations does not correspond to what they say about how they arrived at the conclusion. I don't think this magically grants them this ability, they'll be just more convincing at faking honesty.
- wongarsu 10mo agoHumans do a lot of post-hoc rationalization that does not match their original thought processes either. It is an undesirable feature in LLMs, but I don't think this is a very un-human characteristic Not that it really matters. I don't think this paper starts from a point that assumes that LLMs work like humans, it starts from the assumption that if you give gradient descent a goal to optimize for, it will optimize your network to that goal, with no regard for anything else. So if we just add this one more goal (make an accurate confession), then given enough data that will both work and improve things.
- pfortuny 10mo agoHonest question: > Anthropic showed that LLMs don't understand their own thought processes Where can I find this? I am really interested in that. Thanks.
- encyclopedism 10mo agoWell algorithms don't think. That's what LLM's are. Your digital thermometer doesn't think either.
- pfortuny 10mo agoI was asking for a technical argument against that spurious use of the term.
- roywiggins 10mo agoThe question is more whether LLMs can accurately report their internal operations, not whether any of that counts as "thinking." Simple algorithms can, eg, be designed to report whether they hit an exceptional case and activated a different set of operations than usual.
- BaconVonPork 10mo agoThat's basically a variant of the halting problem and what you hope to get is a supervisor responding. If people expected this I don't think they would be as confused about the difference between statistical analysis of responses requiring emotions to be convincing and an LLM showing atonement.
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- roywiggins 10mo agohttps://www.anthropic.com/research/tracing-thoughts-language-model https://www.anthropic.com/research/tracing-thoughts-language... > Claude, on occasion, will give a plausible-sounding argument designed to agree with the user rather than to follow logical steps. We show this by asking it for help on a hard math problem while giving it an incorrect hint. We are able to “catch it in the act” as it makes up its fake reasoning, providing a proof of concept that our tools can be useful for flagging concerning mechanisms in models... > Claude seems to be unaware of the sophisticated "mental math" strategies that it learned during training. If you ask how it figured out that 36+59 is 95, it describes the standard algorithm involving carrying the 1. This may reflect the fact that the model learns to explain math by simulating explanations written by people, but that it has to learn to do math "in its head" directly, without any such hints, and develops its own internal strategies to do so.
- jerf 10mo agoHumans don't understand their thought process either. In general, neural nets do not have insight into what they are doing, because they can't. Can you tell me what neurons fired in the process of reading this text? No. You don't have access to that information. We can recursively model our own network and say something about which regions of the brain are probably involved due to other knowledge, but that's all a higher-level model. We have no access to our own inner workings, because that turns into an infinite regress problem of understanding our understanding of our understanding of ourselves that can't be solved. The terminology of this next statement is a bit sloppy since this isn't a mathematics or computer science dissertation but rather a comment on HN, but: A finite system can not understand itself. You can put some decent mathematical meat on those bones if you try and there may be some degenerate cases where you can construct a system that understands itself for some definition of "understand", but in the absence of such deliberation and when building systems for "normal tasks" you can count on the system not being able to understand itself fully by any reasonably normal definition of "understand". I've tried to find the link for this before, but I know it was on HN, where someone asked an LLM to do some simple arithmetic, like adding some numbers, and asked the LLM to explain how it was doing it. They also dug into the neural net activation itself and traced what neurons were doing what. While the LLM explanation was a perfectly correct explanation of how to do elementary school arithmetic, what the neural net actually did was something else entirely based around how neurons actually work, and basically it just "felt" its way to the correct answer having been trained on so many instances already. In much the same way as any human with modest experience in adding two digit numbers doesn't necessarily sit there and do the full elementary school addition algorithm but jumps to the correct answer in fewer steps by virtue of just having a very trained neural net. In the spirit of science ultimately being really about "these preconditions have this outcome" rather than necessarily about "why", if having a model narrate to itself about how to do a task or "confess" improves performance, then performance is improved and that is simply a brute fact, but that doesn't mean the naive human understanding about why such a thing might be is correct.
- hnuser123456 10mo agoMakes me wonder if one could train a "neural net surgeon" model which can trace activations in another live model and manipulate it according to plain language instructions.
- codemac 10mo agoPlease reread (or.. read) the paper. They do not make that mistake, specifically section 7.1. A reward function (R) may be hackable by a model's response, but when asked to confess it is easier to get an honest confession reward function (Rc) because you have the response with all the hacking in front of you, and that gives the Rc more ability to verify honesty than R had to verify correctness. There are human examples you could construct (say, granting immunity for better confessions), but they don't map well to this really fascinating insight with LLMs.
- oytis 10mo agoDo these models really lie or do they only do what they are supposed to do - produce text that is statistically similar to the training set, but not in the training set (and thus can include false/made up statements)? Now they add another run on top of it that is in principle prone to the same issues, except they reward the model for factuality instead of likeability. This is cool, but why not apply the same reward strategy to the answer itself?
- catigula 10mo agoThey really lie. Not on purpose; because they are trained on rewards that favor lying as a strategy. Othello-GPT is a good example to understand this. Without explicit training, but on the task of 'predicting moves on an Othello board', Othello-GPT spontaneously developed the strategy of 'simulate the entire board internally'. Lying is a similar emergent, very effective strategy for reward.
- Neywiny 10mo agoNot sure if that counts as lying but I've heard that an ML model (way before all this GPT LLM stuff) learned to classify images based on the text that was written. For an obfuscated example, it learned to read "stop", "arrêt", "alto", etc. on a stop sign instead of recognizing the red octagon with white letters. Which naturally does not work when the actual dataset has different text.
- catigula 10mo agoThat does feel a little more like over-fitting, but you might be able to argue that there's some philosophical proximity to lying. I think, largely, the Pre-training -> Post-training -> Safety/Alignment training pipeline would obviously produce 'lying'. The trainings are in a sort of mutual dissonance.
- Jon_Lowtek 10mo agotypographic attacks against vision-language models are still a thing with more recent models like GPT4-V: https://arxiv.org/abs/2402.00626 https://arxiv.org/abs/2402.00626
- lloydatkinson 10mo agoWhat is this? > Assistant: chain-of-thought Does every LLM have this internal thing it doesn't know we have access to?
- Tzt 10mo agoYes, absolute majority of new ones use CoTs, long chain of reasoning you don't see. Also some of them use such a weird style of talking in them e.g. o3 talks about watchers and marinade, and cunning schemes https://www.antischeming.ai/snippets https://www.antischeming.ai/snippets gpt5 gets existential about seahorses https://x.com/blingdivinity/status/1998590768118731042 https://x.com/blingdivinity/status/1998590768118731042 I remember one where gpt5 spontaneously wrote a poem about deception in its CoT and then resumed like nothing weird happened. But I can't find mentions of it now.
- DenisM 10mo agoGibberish can be the model using contextual embeddings. These are not supposed to Make sense. Or it could be trying to develop its own language to avoid detection. The deception part is spooky too. It’s probably learning that from dystopian AI fiction. Which raises the questions if models can acquire injected goals from the training set.
- DenisM 10mo ago> But the user just wants answer; they'd not like; but alignment. And there it is - the root of the problem. For whatever reason the model is very keen to produce an answer that “they” will like. This desire to produce is intrinsic but alignment is extrinsic.
- catigula 10mo agoYes, they're purposely not 'trained on' chain-of-thought to avoid making it useless for interpretability. As a result, some can find it epistemically shocking if you tell them you can see their chain-of-thought. More recent models are clever enough to know you can see their chain-of-thought implicitly without training.
- tummler 10mo agoSomeone build an LLM confessional site where a human user acts as the priest and an LLM joins the chat to confess its sins.
- andrepd 10mo agoBlessings of the state! Blessings of the masses!
- carsoon 10mo agoWe could first put the LLMs in very difficult situations like the trolley problem and other variants of this, then once they make their decisions they can explain to us how their choice weighs on their mind and how they are not sure if they did the correct thing.
- carsoon 10mo agoI built it, now you can forgive all the llms for their misdeeds: https://llmpriest.carsho.dev/ https://llmpriest.carsho.dev/ https://news.ycombinator.com/item?id=46251110 https://news.ycombinator.com/item?id=46251110
- tummler 10mo agoLOL. Is this working from a prompt to make up a fictitious sin? Because if what it's telling me is true...
- skybrian 10mo agoIt seems like “self-criticism” would be a better way to describe what they are training the LLM to do than “confession?” The LLM is not being directly trained to accurately reveal its chain of thought or internal calculations. But it does have access to its chain of thought and tool calls when generating the self-criticism, and perhaps reporting on what it actually did in the chain-of-thought is an “easier” way to score higher on self-criticism? Can this result in improved “honesty?” Maybe in the limited sense of accurately reporting what happened previously in the chat session.
- pegasus 10mo agoYou're totally right, "self-criticism" would be more appropriate. I wonder if researchers, in their desire to anticipate a hoped-for AGI, tend to pick words which make these models feel more human-like than they really are. Another good example is "hallucination" instead of "confabulation".
- dennisy 10mo agoAre we only able to think of these systems as some form of human and probe them from the outside like a therapist? Surely these sorts of problems must be worked upon from a mathematical standpoint.
- measurablefunc 10mo agoLLMs can not "lie", they do not "know" anything, and certainly can not "confess" to anything either. What LLMs can do is generate numbers which can be constructed piecemeal from some other input numbers & other sources of data by basic arithmetic operations. The output number can then be interpreted as a sequence of letters which can be imbued with semantics by someone who is capable of reading and understanding words and sentences. At no point in the process is there any kind of awareness that can be attributed to any part of the computation or the supporting infrastructure other than whoever started the whole chain of arithmetic operations by pressing some keys on some computer connected to the relevant network of computers for carrying out the arithmetic operations. If you think this is reductionism you should explain where exactly I have reduced the operations of the computer to something that is not a correct & full fidelity representation of what is actually happening. Remember, the computer can not do anything other than boolean algebra so make sure to let me know where exactly I made an error about the arithmetic in the computer.
- nazgul17 10mo agoCan't you say the same of the human brain, given a different algorithm? Granted, we don't know the algorithm, but nothing in the laws of physics implies we couldn't simulate it on a computer. Aren't we all programs taking analog inputs and spitting actions? I don't think what you presented is a good argument for LLMs not "know"ing, in some meaning of the word.
- measurablefunc 10mo agoWhat meaning of "knowing" attributes understanding to a sequence of boolean operations?
- cmccand1 10mo agoHuman brains depend on neurons and "neuronal arithmetic". In fact, their statements are merely "neuronal arithmetic" that gets converted to speech or writing that get imbued with semantic meaning when interpreted by another brain. And yet, we have no problem attributing dishonesty or knowledge to other humans.