Promoted to curated: Formalizing what it means for transformers to learn “the underlying world model” when engaging in next-token prediction tasks seems pretty useful, in that it’s an abstraction that I see used all the time when discussing risks from models where the vast majority of the compute was spent in pre-training, where the details usually get handwaived. It seems useful to understand what exactly we mean by that in more detail.
I have not done a thorough review of this kind of work, but it seems to me that also others thought the basic ideas in the work hold up, and I thought reading this post gave me crisper abstractions to talk about this kind of stuff in the future.
Promoted to curated: Formalizing what it means for transformers to learn “the underlying world model” when engaging in next-token prediction tasks seems pretty useful, in that it’s an abstraction that I see used all the time when discussing risks from models where the vast majority of the compute was spent in pre-training, where the details usually get handwaived. It seems useful to understand what exactly we mean by that in more detail.
I have not done a thorough review of this kind of work, but it seems to me that also others thought the basic ideas in the work hold up, and I thought reading this post gave me crisper abstractions to talk about this kind of stuff in the future.