Graph-Guided Reasoning for Multi-Hop Question Answering in Large
  Language Models
Jinyoung Park Ameen Patel Omar Zia Khan Hyunwoo J. Kim Joo-Kyung Kim
Chain-of-Thought (CoT) prompting has boosted the multi-step reasoning
capabilities of Large Language Models (LLMs) by generating a series of
rationales before the final answer. We analyze the reasoning paths generated by
CoT and find two issues in multi-step reasoning: (i) Generating rationales
irrelevant to the question, (ii) Unable to compose subquestions or queries for
generating/retrieving all the relevant information. To address them, we propose
a graph-guided CoT prompting method, which guides the LLMs to reach the correct
answer with graph representation/verification steps. Specifically, we first
leverage LLMs to construct a "question/rationale graph" by using knowledge
extraction prompting given the initial question and the rationales generated in
the previous steps. Then, the graph verification step diagnoses the current
rationale triplet by comparing it with the existing question/rationale graph to
filter out irrelevant rationales and generate follow-up questions to obtain
relevant information. Additionally, we generate CoT paths that exclude the
extracted graph information to represent the context information missed from
the graph extraction. Our graph-guided reasoning method shows superior
performance compared to previous CoT prompting and the variants on multi-hop
question answering benchmark datasets.