GenDec: A robust generative Question-decomposition method for Multi-hop
  reasoning
Jian Wu Linyi Yang Yuliang Ji Wenhao Huang Börje F. Karlsson Manabu Okumura
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.