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.

翻译：在文本问答系统中，复杂问题通常需要检索多条文本事实链并进行多步推理。然而现有基准局限于单链或单跳检索场景。本文提出Graph-Hop——一种用于复杂问答的新型多链多跳检索与推理范式。我们构建了名为ReasonGraphQA的新基准，为复杂问题提供显式的细粒度证据图以支持可解释推理、全面且详细的推理。ReasonGraphQA在推理多样性和规模上也展现出优势。此外，我们提出了一种强图跳基线方法——双向图检索（BGR）——用于在知识推理和问答中生成文本证据的解释图。我们在ReasonGraphQA上全面评估了现有证据检索与推理模型。实验表明，Graph-Hop是回答复杂问题的一个有前景的方向，但仍存在一定局限。我们进一步研究了应对这些挑战的缓解策略并探讨了未来方向。