GenDec: A robust generative Question-decomposition method for Multi-hop reasoning

• URL: http://arxiv.org/abs/2402.11166v1
• Date: Sat, 17 Feb 2024 02:21:44 GMT
• Title: GenDec: A robust generative Question-decomposition method for Multi-hop reasoning
• Authors: Jian Wu and Linyi Yang and Yuliang Ji and Wenhao Huang and B\"orje F. Karlsson and Manabu Okumura
• Abstract summary: Multi-hop QA involves step-by-step reasoning to answer complex questions. Existing large language models'(LLMs) reasoning ability in multi-hop question answering remains exploration. It is unclear whether LLMs follow a desired reasoning chain to reach the right final answer.
• Score: 32.12904215053187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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