In Textual question answering (TQA) systems, complex questions often require
retrieving multiple textual fact chains with multiple reasoning steps. While
existing benchmarks are limited to single-chain or single-hop retrieval
scenarios. In this paper, we propose to conduct Graph-Hop -- a novel
multi-chains and multi-hops retrieval and reasoning paradigm in complex
question answering. We construct a new benchmark called ReasonGraphQA, which
provides explicit and fine-grained evidence graphs for complex questions to
support interpretable reasoning, comprehensive and detailed reasoning. And
ReasonGraphQA also shows an advantage in reasoning diversity and scale.
Moreover, We propose a strong graph-hop baseline called Bidirectional Graph
Retrieval (BGR) method for generating an explanation graph of textual evidence
in knowledge reasoning and question answering. We have thoroughly evaluated
existing evidence retrieval and reasoning models on the ReasonGraphQA.
Experiments highlight Graph-Hop is a promising direction for answering complex
questions, but it still has certain limitations. We have further studied
mitigation strategies to meet these challenges and discuss future directions.

本文提出了 Graph-Hop 多链式及多跳式检索和推理范式，建立了新的 Benchmark ReasonGraphQA 来支持可解释的推理，论证其在推理多样性和规模方面的优势，并在现有证据检索和推理模型上进行了彻底的评估。