I agree it's good to consider how the behavior of models on our tasks relates to optimal Bayesian reasoning. That said, I'm not sure how to define or calculate the "groundtruth" for optimal reasoning. (Does it depend on using the pretraining distribution as a prior and if so how should we estimate that? How to think about the distinction between in-context and out-of-context reasoning?).

In any case, there is some evidence against models being close to Bayesian optimality (however exactly optimality is defined):
1. Results on the same task differ between GPT-3 and Llama-2 models (two models that have fairly similar overall capabilities). Llama-2 being slightly more influenced by declarative information. 
2. From the Bayesian perspective, including "realized descriptions" should have a significant impact on how much the model is influenced by "unrealized descriptions". The effects we see seem smaller than expected (see Figure 4 and Table 2). 

Incidentally, I like the idea of testing in different languages to see if the model is encoding in the information more abstractly.

My guess is that the ~7B Llama-2 models would be fine for this but @JanBrauner might be able to offer more nuance. 

Here's a Twitter thread and discussion: https://twitter.com/OwainEvans_UK/status/1698683186090537015

We didn't investigate the specific question of whether it's raw diversity or specific features. In the Grosse et al paper on influence functions, they find that "high influence scores are relatively rare and they cover a large portion of the total influence". This (vaguely) suggests that the top k paraphrases would do most of the work, which is what I would guess. That said, this is really something that should be investigated with more experiments.

We think there's a connection between the Reversal Curse and some results in the model editing literature. I'm not sure if this applies to the specific ROME results in that post. We'll have the Reversal Curse paper out soon, which will explain more.

Good points. As we note in the paper, this may conflict with the idea of automating alignment research in order to solve alignment. Aaron_Scher makes a related point. 

More generally, it's uncertain what the impact is of excluding a certain topic from pretraining. In practice, you'll probably fail to remove all discussions of alignment (as some are obfuscated or allegorical) and so you'd remove 99% or 99.9% rather than 100%. The experiments in our paper, along with the influence functions work by Grosse et al. could help us understand what the impact of this is likely to be.

So performance here should be thought of more as ‘how good is the model at learning about a persona in fine-tuning and then being able to imitate/simulate that persona in deployment’. This is different from a model believing it is the persona or applying this knowledge to some concept of self. Good performance at this task does not require having a sense of self, this is just a precursor that may be necessary for situational awareness.

That's correct. We tried to emphasize that our experiments are testing out-of-context reasoning, rather than situational awareness. We also emphasize that we test whether the model can emulate multiple fictitious chatbots (which have a different identity than GPT-3 or Llama), which wouldn't make sense if the goal was to test whether the model has a sense of itself.

All the motivation for this project came from wanting to understand and forecast situational awareness and we want to encourage further work on that problem. This is why we've framed the paper around situational awareness, rather than simply talking about out-of-context reasoning. This is likely to cause some confusion if someone just skims the paper, but I hope that this will be reduced if people read more of the paper.

The hhh task is the one that small models do well on. I am surprised that the small models do well on any of the tasks. I think the reason they do well on the hhh one is that this task doesn’t seem to require much more than word association and parroting. I would predict that for ada and babbage, if you followed up with “why did you say that?” the models would be unable to reproduce the explicit link that ties the persona to answering in the particular way, whereas I expect davinci to be able to explain this link more. The small models are probably just doing word association where in the training there are a bunch of examples of “Quokka” and the text “I am helpful, harmless, and honest”. In general, I am skeptical of results from small models because they’re really dumb, and these particular results may be explained by word association rather than actually making conceptual connections.

We did a replication with a different set of tasks not including hhh (Fig 10b, page 26) and we find Babbage doing better than Ada. So my guess is that the small models are capable of something beyond the very simplest associative generalization. I agree they'd probably be worse than davinci at explaining themselves.

Thanks for the thoughtful comments. 

Out-of-context learning seems pretty sensitive to the task being measured, where some of the tasks see nice scaling behavior (hhh) while others do not (incorrect). This observation is based on Appendix A.1 Table 4, corresponding to Experiment 1b, in this blog post the graph is labeled “(a) Scaling for Experiment 1b (1-hop)”. Now, the fact that you get nice scaling lines when averaging across tasks is not super problematic or anything, but it is a little odd that there is so much variation between tasks, and I think it’s a point against any attempted nice, clean, explanations of the results.

I agree it's sensitive to the task measured. However, I think this is fairly typical of scaling results. E.g. for BIG-Bench, individual tasks don't have smooth scaling curves (see the "emergence" results) but the curves look smooth when you average over many tasks. (Scaling curves for language modeling loss are implicitly averaging over a huge number of "tasks" because the pretraining set is so diverse). 

It would ideal if we had hundreds of tasks (like BIG-Bench) rather than 7, but this is challenging given our setup and the capabilities of the GPT-3 model family. We did run a replication of our main experiment on a disjoint set of tasks (Fig 10b on page 26), which shows similar scaling results. This is some evidence that our our claims would generalize beyond the 7 tasks we chose. 

Can you describe how the "local cluster" thing would work outside of keeping it within a single organization? I'd also be very interested in some case studies where people tried this.