Leveraging Structured Information for Explainable Multi-hop Question
  Answering and Reasoning
Ruosen Li Xinya Du
Neural models, including large language models (LLMs), achieve superior
performance on multi-hop question-answering. To elicit reasoning capabilities
from LLMs, recent works propose using the chain-of-thought (CoT) mechanism to
generate both the reasoning chain and the answer, which enhances the model's
capabilities in conducting multi-hop reasoning. However, several challenges
still remain: such as struggling with inaccurate reasoning, hallucinations, and
lack of interpretability. On the other hand, information extraction (IE)
identifies entities, relations, and events grounded to the text. The extracted
structured information can be easily interpreted by humans and machines
(Grishman, 2019). In this work, we investigate constructing and leveraging
extracted semantic structures (graphs) for multi-hop question answering,
especially the reasoning process. Empirical results and human evaluations show
that our framework: generates more faithful reasoning chains and substantially
improves the QA performance on two benchmark datasets. Moreover, the extracted
structures themselves naturally provide grounded explanations that are
preferred by humans, as compared to the generated reasoning chains and
saliency-based explanations.