Regarding the claim that finetuning on data with property $P$ will lead models to ‘understand’ (scare-quotes omitted from now on...) both $P$ and not $P$ better, thanks. I see better where the post is coming from.
However, I don’t necessarily think that we get the easier elicitation of not $P$. There are reasons to believe finetuning is simply resteering the base model and not changing its understanding at all. For example, there are far more training steps in pretraining vs. finetuning. Even if finetuning is shaping a model’s understanding of $P$, in an RLHF setup you’re generally seeing two responses, one with less $P$ and one with more $P$, and I’m not sure that I buy that the model’s inclination to output not $P$ responses can increase given there are no gradients from not $P$ cases. There are in red-teaming setups though and I think the author should register predictions in advance and then blind test various base models and finetuned models for the Waluigi Effect.
I meant your first point.
Regarding the claim that finetuning on data with property $P$ will lead models to ‘understand’ (scare-quotes omitted from now on...) both $P$ and not $P$ better, thanks. I see better where the post is coming from.
However, I don’t necessarily think that we get the easier elicitation of not $P$. There are reasons to believe finetuning is simply resteering the base model and not changing its understanding at all. For example, there are far more training steps in pretraining vs. finetuning. Even if finetuning is shaping a model’s understanding of $P$, in an RLHF setup you’re generally seeing two responses, one with less $P$ and one with more $P$, and I’m not sure that I buy that the model’s inclination to output not $P$ responses can increase given there are no gradients from not $P$ cases. There are in red-teaming setups though and I think the author should register predictions in advance and then blind test various base models and finetuned models for the Waluigi Effect.