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In Textual question answering (TQA) systems, complex questions often requireretrieving multiple textual fact chains with multiple reasoning steps. Whileexisting benchmarks are limited to single-chain or single-hop retrievalscenarios. In this paper, we propose to conduct Graph-Hop -- a novelmulti-chains and multi-hops retrieval and reasoning paradigm in complexquestion answering. We construct a new benchmark called ReasonGraphQA, whichprovides explicit and fine-grained evidence graphs for complex questions tosupport interpretable reasoning, comprehensive and detailed reasoning. AndReasonGraphQA also shows an advantage in reasoning diversity and scale.Moreover, We propose a strong graph-hop baseline called Bidirectional GraphRetrieval (BGR) method for generating an explanation graph of textual evidencein knowledge reasoning and question answering. We have thoroughly evaluatedexisting evidence retrieval and reasoning models on the ReasonGraphQA.Experiments highlight Graph-Hop is a promising direction for answering complexquestions, but it still has certain limitations. We have further studiedmitigation strategies to meet these challenges and discuss future directions.

Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database