用提示词优化搜索策略，减少大模型在科研中的幻觉问题。
Prompt-Based Monte Carlo Tree Search for Mitigating Hallucinations in Large Models
- 通过提示词动态调整探索参数，平衡搜索深度与广度
- 在SciEval四个子集上优于现有方法，降低幻觉率
- 适合需要高可信推理的科研场景应用
随着人工智能领域大模型的快速发展，如何提升其在科研复杂问题中的应用能力仍是待解难题。本文提出一种基于提示词的改进蒙特卡洛树搜索（Improved MCTS）方法。在模拟搜索阶段，引入探索参数的动态调整与自适应选择策略，更好平衡探索与利用，从而缓解幻觉现象。以SciEval数据集的四个子集为测试对象，将Glm-4-flash+Improved MCTS方法与多个现有模型进行对比。结果表明，该方法表现更优，为大模型在科研领域的应用提供了新思路与方法。
原文摘要 · Abstract (English)
With the rapid development of large models in the field of artificial intelligence, how to enhance their application capabilities in handling complex problems in the field of scientific research remains a challenging problem to be solved. This study proposes an improved Monte Carlo Tree Search (MCTS) method based on prompt words. In the simulation search stage, it introduces dynamic adjustment of exploration parameters and adaptive selection strategies, which can better balance exploration and exploitation, thereby reducing the hallucination phenomenon. This paper takes the four subsets of the SciEval dataset as the test objects, and compares the Glm-4-flash+Improved MCTS method with the methods of several existing models. The results show that the Improved MCTS method performs better, providing new ideas and methods for the application of large models in the field of scientific research.
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