Think-to-Talk or Talk-to-Think? When LLMs Come Up with an Answer in Multi-Hop Arithmetic Reasoning

Abstract

This study investigates the incremental, internal problem-solving process of language models (LMs) with arithmetic multi-hop reasoning as a case study. We specifically investigate when LMs internally resolve sub/whole problems through first reading the problem statements, generating reasoning chains, and achieving the final answer to mechanistically interpret LMs' multi-hop problem-solving process. Our experiments reveal a systematic incremental reasoning strategy underlying LMs. They have not derived an answer at the moment they first read the problem; instead, they obtain (sub)answers while generating the reasoning chain. Therefore, the generated reasoning chains can be regarded as faithful reflections of the model's internal computation.