Although large language models demonstrate remarkable question-answering performances, revealing the intermediate reasoning steps that the models faithfully follow remains challenging. In this paper, we propose FAME (FAithful question answering with MontE-carlo planning) to answer questions based on faithful reasoning steps. The reasoning steps are organized as a structured entailment tree, which shows how premises are used to produce intermediate conclusions that can prove the correctness of the answer. We formulate the task as a discrete decision-making problem and solve it through the interaction of a reasoning environment and a controller. The environment is modular and contains several basic task-oriented modules, while the controller proposes actions to assemble the modules. Since the search space could be large, we introduce a Monte-Carlo planning algorithm to do a look-ahead search and select actions that will eventually lead to high-quality steps. FAME achieves state-of-the-art performance on the standard benchmark. It can produce valid and faithful reasoning steps compared with large language models with a much smaller model size.

翻译：尽管大型语言模型展现出卓越的问答表现，但揭示模型忠实遵循的中间推理步骤仍具挑战性。本文提出FAME（基于蒙特卡洛规划的忠实问答）框架，通过忠实推理步骤回答问题。推理步骤被组织为结构化蕴含树，展示前提如何用于生成能够证明答案正确性的中间结论。我们将该任务构建为离散决策问题，通过推理环境与控制器间的交互来求解。环境采用模块化设计，包含多个基础任务导向模块，而控制器则提出动作以组合这些模块。由于搜索空间可能较大，我们引入蒙特卡洛规划算法进行前瞻搜索，最终选择能生成高质量步骤的动作序列。FAME在标准基准测试中达到当前最优性能。相比大型语言模型，其能以更小的模型规模生成有效且忠实的推理步骤。