多路径协作反思机制提升大模型科学推理准确率
Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents
- 多路径并行，每条路径由反应型与反思型代理协同工作
- 零样本和少样本测试中，物理与数学任务准确率显著提升
- 无需额外训练，通过对话聚合实现跨路径知识融合
基于大语言模型的智能体在科学推理任务中展现出潜力，但在处理复杂问题时常面临准确性不足与思维退化的问题。为此，我们提出反应与反思多路径推理（RR-MP）框架，旨在增强大模型的推理能力。该方法通过多路径推理机制，每条路径包含一个反应型代理和一个反思型代理，协同防止单一代理依赖导致的思维退化。同时，RR-MP框架无需额外训练，利用每条路径的多个对话实例，并通过独立摘要器整合各路径见解，融合多样化视角以强化推理。我们在涉及道德情境、大学物理和数学的任务上进行了零样本和少样本评估。实验结果表明，该方法优于基线模型，验证了其在复杂科学推理任务中的有效性与优势。
原文摘要 · Abstract (English)
Agents have demonstrated their potential in scientific reasoning tasks through large language models. However, they often face challenges such as insufficient accuracy and degeneration of thought when handling complex reasoning tasks, which impede their performance. To overcome these issues, we propose the Reactive and Reflection agents with Multi-Path Reasoning (RR-MP) Framework, aimed at enhancing the reasoning capabilities of LLMs. Our approach improves scientific reasoning accuracy by employing a multi-path reasoning mechanism where each path consists of a reactive agent and a reflection agent that collaborate to prevent degeneration of thought inherent in single-agent reliance. Additionally, the RR-MP framework does not require additional training; it utilizes multiple dialogue instances for each reasoning path and a separate summarizer to consolidate insights from all paths. This design integrates diverse perspectives and strengthens reasoning across each path. We conducted zero-shot and few-shot evaluations on tasks involving moral scenarios, college-level physics, and mathematics. Experimental results demonstrate that our method outperforms baseline approaches, highlighting the effectiveness and advantages of the RR-MP framework in managing complex scientific reasoning tasks.
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