Research associate at the University of Lancaster, studying AI safety.
My current views on AI:
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Ah I see ur point. Yh I think that’s a natural next step. Why do you think it not very interesting to investigate? Being able to make very accurate inferences given the evidence at hand seems important for capabilities, including alignment relevant ones?
Thanks for having a read!
Do you expect abductive reasoning to be significantly different from deductive reasoning? If not, (and I put quite high weight on this,) then it seems like (Berglund, 2023) already tells us a lot about the cross-context abductive reasoning capabilities of LLMs. I.e. replicating their methodology wouldn't be very exciting.
Berglund et. al. (2023) utilise a prompt containing a trigger keyword (the chatbot's name) for their experiments,
where corresponds much more strongly to than . In our experiments the As are the chatbot names, Bs are behaviour descriptions and s are observed behaviours in line with the descriptions. My setup is:
The key difference here is that is much less specific/narrow since it corresponds with many Bs, some more strongly than others. This is therefore a (Bayesian) inference problem. I think it's intuitively much easier to forward reason given a narrow trigger to your knowledge base (eg. someone telling you to use the compound interest equation) than to figure out which parts of your knowledge base are relevant (you observe numbers 100, 105, 110.25 and asking which equation would allow you to compute the numbers).
Similarly, for the RL experiments (experiment 3) they use chatbot names as triggers and therefore their results are relevant to narrow backdoor triggers rather than more general reward hacking leveraging declarative facts in training data.
I am slightly confused whether their reliance on narrow triggers is central to the difference between abductive and deductive reasoning. Part of the confusion is because (Beysian) inference itself seems like an abductive reasoning process. Popper and Stengel (2024) discuss this a little bit under page 3 and footnote 4 (process of realising relevance).
One difference that I note here is that abductive reasoning is uncertain / ambiguous; maybe you could test whether the model also reduces its belief of competing hypotheses (c.f. 'explaining away').
Figures 1 and 4 attempt to show that. If the tendency to self-identify as the incorrect chatbot falls with increasing k and iteration than the models are doing this inference process right. In Figures 1 and 4, you can see that the tendency to incorrectly identify as a pangolin falls in all non-pangolin tasks (except for tiny spikes sometimes at iteration 1).
Can you say more? I don't think I see why that would be.
When we ask whether some CoT is faithful, we mean something like: "Does this CoT allow us to predict the LLM's response more than if there weren't a CoT?"
The simplest reason I can think of for why CoT improves performance yet doesn't allow predictability is that the improvement is mostly a result of extra computation and the content of the CoT does not matter very much, since the LLM still doesn't "understand" the Cot it produces the same way we do.
If you are using outcomes-based RL with a discount factor ( in ) or some other penalty for long responses, there is optimisation pressure towards using the abstractions in your reasoning process that most efficiently get you from the input query to the correct response.
NAH implies that the universe lends itself to natural abstractions, and therefore most sufficiently intelligent systems will think in terms of those abstractions. If the NAH is true, those abstractions will be the same abstractions that other sufficiently intelligent systems (humans?) have converged towards, allowing these systems to interpret each other's abstractions.
I naively expect o1's CoT to be more faithful. It's a shame that OpenAI won't let researchers access o1 CoT; otherwise, we could have tested it (although the results would be somewhat confounded if they used process supervision as well).
Thanks for the reply.
Yes, I did see that paragraph in the paper. My point was I intuitively expected it to be better still. O1-preview is also tied with claude 3.5 sonnet on simplebench and also arc-agi, while using a lot more test time compute. However this is their first generation of reasoning models so any conclusions may be premature.
Re CoT monitoring:
Agree with 4, it will defeat the purpose of explicit CoTs.
Re. 1, I think outcomes based RL (with some penalty for long responses) should somewhat mitigate this problem, at least if NAH is true?
Re 2-3, Agree unless we use models that are incapable of deceptive reasoning without CoT (due to number of parameters or training data). Again would love to see more rigorous research on this. That way any deceptive reasoning has to be through the CoT and can be caught. I suspect such reasoners may not be as good as reasoners using larger models though so this may not work. (o1-mini is quite worse than o1 preview and even sonnet on many tasks).
Nice post!
Regarding o1 like models: I am still unsure how to draw the boundary between tasks that see a significant improvement with o1 style reasoning and tasks that do not. This paper sheds some light on the kinds of tasks that benefit from regular COT. However, even for mathematical tasks, which should benefit the most from CoT, o1-preview does not seem that much better than other models on extraordinarily difficult (and therefore OOD?) problems. I would love to see comparisons of o1 performance against other models in games like chess and Go.
Also, somewhat unrelated to this post, what do you and others think about x-risk in a world where explicit reasoners like o1 scale to AGI. To me this seems like one of the safest forms of AGI, since much of the computation is happening explicitly, and can be checked/audited by other AI systems and humans.
My understanding is something like:
OpenAI RL fine-tuned these language models against process reward models rather than outcome supervision. However, process supervision is much easier for objective tasks such as STEM question answering, therefore the process reward model is underspecified for other (out of distribution) domains. It's unclear how much RL fine-tuning is performed against these underspecified reward models for OOD domains. In any case, when COTs are sampled from these language models in OOD domains, misgeneralization is expected. I don't know how easily this is fixable with standard RLHF / outcome reward models (although I don't expect it to be too difficult), but it seems like instead of fixing it they have gone the route of, we'll keep it unconstrained and monitor it. (Of course, there may be other reasons as well such as to prevent others from fine-tuning on their COTs).
I'm a little concerned that even if they find very problematic behaviour, they will blame it on clearly expected misgeneralization and therefore no significant steps will be taken, especially because there is no reputational damage (This concern is conditional on the assumption of very little outcomes-based supervision and mostly only process supervision on STEM tasks).
One thing I’d be bearish on is visibility into the latest methods being used for frontier AI methods, which would downstream reduce the relevance of alignment research except for the research within the manhattan-like project itself. This is already somewhat true of the big labs eg. methods used for o1 like models. However, there is still some visibility in the form of system cards and reports which hint at the methods. When the primary intention is racing ahead of China, I doubt there will be reports discussing methods used for frontier systems.