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.

通过提出可解释问答的生成性问题分解方法（GenDec），本文试图通过生成独立完整的子问题来增强大型语言模型在多跳问题答案推理能力，并通过结合 GenDec 与小规模 QA 系统以及 GPT-4 等最先进的 LLM，进行基于 HotpotQA、2WikihopMultiHopQA、MuSiQue 和 PokeMQA 数据集的两个实验来展示 GenDec 的影响、泛化性和鲁棒性。

GenDec：一种用于多跳推理的稳健生成式问题分解方法

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

Interpreting the reasoning process from questions to answers poses a
challenge in approaching explainable QA. A recently proposed structured
reasoning format, entailment tree, manages to offer explicit logical deductions
with entailment steps in a tree structure. To generate entailment trees, prior
single pass sequence-to-sequence models lack visible internal decision
probability, while stepwise approaches are supervised with extracted single
step data and cannot model the tree as a whole. In this work, we propose RLET,
a Reinforcement Learning based Entailment Tree generation framework, which is
trained utilising the cumulative signals across the whole tree. RLET
iteratively performs single step reasoning with sentence selection and
deduction generation modules, from which the training signal is accumulated
across the tree with elaborately designed aligned reward function that is
consistent with the evaluation. To the best of our knowledge, we are the first
to introduce RL into the entailment tree generation task. Experiments on three
settings of the EntailmentBank dataset demonstrate the strength of using RL
framework.

本文提出了一种基于强化学习的蕴含树生成框架 RLET，通过句子选择和推理生成模块进行单步推理，并积累整个树的训练信号，实现了针对解释性 QA 中的逻辑推理过程，并且在三个数据集上的实验表明了使用强化学习框架的优点。