Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts. However, Existing large language models'(LLMs) reasoning ability in multi-hop question answering remains exploration, which is inadequate in answering multi-hop questions. Moreover, it is unclear whether LLMs follow a desired reasoning chain to reach the right final answer. In this paper, we propose a \textbf{gen}erative question \textbf{dec}omposition method (GenDec) from the perspective of explainable QA by generating independent and complete sub-questions based on incorporating additional extracted evidence for enhancing LLMs' reasoning ability in RAG. To demonstrate the impact, generalization, and robustness of Gendec, we conduct two experiments, the first is combining GenDec with small QA systems on paragraph retrieval and QA tasks. We secondly examine the reasoning capabilities of various state-of-the-art LLMs including GPT-4 and GPT-3.5 combined with GenDec. We experiment on the HotpotQA, 2WikihopMultiHopQA, MuSiQue, and PokeMQA datasets.
SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering
City University of Hong Kong（香港城市大学）；Xi’an Jiaotong University（西安交通大学）；Zhejiang University of Technology（浙江工业大学）；Michigan State University（密歇根州立大学）；Jilin University（吉林大学）
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective
CAS Key Lab of Network Data Science and Technology, ICT, CAS（中国科学院计算技术研究所网络数据科学与技术重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；University of Amsterdam（阿姆斯特丹大学）
National Institute of Informatics（情报学研究所）；Nantes Université（南特大学）；The Graduate University for Advanced Studies（高级研究大学院大学）；The University of Tokyo（东京大学）；EPFL（洛桑联邦理工学院）
DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections
Pohang University of Science and Technology（釜山科学技术大学）；The University of Sydney（悉尼大学）；The University of Western Australia（西澳大学）；The University of Melbourne（墨尔本大学）
Ubiquitous Knowledge Processing Lab (UKP Lab)（普适知识处理实验室（UKP实验室））；Department of Computer Science（计算机科学系）；Hessian Center for AI (hessian.AI)（黑森人工智能中心（hessian.AI））；Technical University of Darmstadt（达姆施塔特工业大学）；cohere（Cohere公司）
MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs
Institute for Logic, Language and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Center for Language and Cognition, University of Groningen（格罗宁根大学语言与认知中心）
