Faithful Reasoning (with LLMs)

arxiv.org · 7,354 words · saved by 1 readers

Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. This entails an unwelcome degree of opacity and compromises performance, especially on problems that are inherently multi-step. To address these limitations, we show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem. Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs, one for selection and one for inference, to produce a valid reasoning trace. Our method carries out a beam search through the space of reasoning traces to improve reasoning quality. We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy, and generates humanly interpretable reasoning traces whose validity can be checked by the user.

August 2022 Faithful Reasoning Using Large Language Models Antonia Creswell1 and Murray Shanahan1 1 DeepMind Although contemporary large language models (LMs) demonstrate impressive question-answering capa- bilities, their answers are typically the product of a single call to the model. This entails an unwelcome…

Faithful Reasoning (with LLMs)

related reading