| My thoughts on CoT and the extent to which it "elicits reasoning" align almost perfectly with the criticism of Rao Kambhampati and his students: https://arxiv.org/abs/2405.04776 Their argument is that CoT can only improve performance of LLMs in reasoning tasks when the prompter already knows the answer and can somehow embed it in their prompt. The paper I link above supports this intuition with empirical results, summarised in the abstract as follows: While our problems are very simple, we only find meaningful performance improvements from chain of thought prompts when those prompts are exceedingly specific to their problem class, and that those improvements quickly deteriorate as the size n of the query-specified stack grows past the size of stacks shown in the examples. We also create scalable variants of three domains commonly studied in previous CoT papers and demonstrate the existence of similar failure modes. Our results hint that, contrary to previous claims in the literature, CoT's performance improvements do not stem from the model learning general algorithmic procedures via demonstrations but depend on carefully engineering highly problem specific prompts. And if that's the case and I need to know the solution to a reasoning task before I can prompt an LLM to solve it- then why do I need to prompt an LLM to solve it? Or, if I'm just asking an LLM to generate the Prolog code I can write myself then what's the point of that? As I argue in another comment, an LLM will only do well in generating correct code if it has seen some sufficient number of examples of the code I'm asking it to generate anyway. So I don't think that CoT, used to generate Prolog, is really adding anything to my capability to solve problems by coding in Prolog. I have no idea how o1 works internally and I prefer not to speculate but it doesn't seem to be some silver bullet that will make LLMs capable of reasoning. |