用蒙特卡洛树搜索提升大模型多跳问答准确性，零样本无需示例。
Zero-Shot Multi-Hop Question Answering via Monte-Carlo Tree Search with Large Language Models
- 基于蒙特卡洛树搜索寻找最优推理路径，避免错误累积。
- 零样本提示下在多个基准上超越现有方法，性能显著提升。
- 适合追求高精度且希望减少人工标注的多跳问答研究者。
大语言模型在多跳问答任务中虽取得进展，但其自回归特性易导致中间推理错误积累。本文提出零样本多跳问答框架MZQA，利用蒙特卡洛树搜索（MCTS）识别最优推理路径，缓解序列推理中的误差传播。不同于以往依赖人工设计少样本示例的方法，本工作采用仅需指令的零样本提示策略，无需领域专业知识。同时引入行为克隆方法（MZQA-BC），基于自生成的MCTS推理轨迹训练，实现推理速度提升超过10倍，性能损失极小。该方法在HotpotQA、2WikiMultihopQA和MuSiQue等标准数据集上验证有效，表现优于现有框架。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have significantly impacted the domain of multi-hop question answering (MHQA), where systems are required to aggregate information and infer answers from disparate pieces of text. However, the autoregressive nature of LLMs inherently poses a challenge as errors may accumulate if mistakes are made in the intermediate reasoning steps. This paper introduces Monte-Carlo tree search for Zero-shot multi-hop Question Answering (MZQA), a framework based on Monte-Carlo tree search (MCTS) to identify optimal reasoning paths in MHQA tasks, mitigating the error propagation from sequential reasoning processes. Unlike previous works, we propose a zero-shot prompting method, which relies solely on instructions without the support of hand-crafted few-shot examples that typically require domain expertise. We also introduce a behavioral cloning approach (MZQA-BC) trained on self-generated MCTS inference trajectories, achieving an over 10-fold increase in reasoning speed with bare compromise in performance. The efficacy of our method is validated on standard benchmarks such as HotpotQA, 2WikiMultihopQA, and MuSiQue, demonstrating that it outperforms existing frameworks.
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