| You’re right of course. The LLM is a calculator continuously predicting a best-fitting next token based on the data it was trained on. If its outputs resemble human reasoning, it’s because the encoding and training process managed to capture those patterns and use them to simulate fitting text. There is no real reasoning happening or second-order thought, other than a simulation of that happening through the mimicry of human writing. LLMs can’t be prompted to perform actual reasoning, but they can be told to generate “thoughts” about what they’re doing that bring out more nuanced detail when they give their answers. This isn’t any more magical than writing out a more thoughtful prompt to get a conditioned answer, it’s just getting the LLM to flesh out the prompt engineering for you in the general direction you want it to go. That seems rather fundamental to me, the idea that with some generic prompting the model tries to fit what it thinks reasoning looks like and can then take advantage of the additional context that would others be buried too deep to influence its answer. I suspect that prompting the model to explore “thought” asks it to go down paths of linguistic connections that are related to the topic but not immediately connected to the answer in a way that would immediately influence the top predictions. Bringing summaries of those connections into the token context is a kind of zero-shot training on their relevancy to forming an answer. To me this is less “reasoning” and more suggestive of the idea that some of the heuristics for data retrieval and question answering we collectively refer to as reasoning have broader applications. |