让大模型自动发现启发式规则，提升复杂规划的准确性与可解释性。
Complex LLM Planning via Automated Heuristics Discovery
- 大模型自动生成启发式函数，指导推理过程中的搜索路径。
- 在多个基准测试中，部分数据集准确率接近翻倍，优于现有方法。
- 无需额外训练，生成的规则可解释，适合需要透明决策的场景。
我们研究如何增强大语言模型（LLMs）在复杂规划任务中的表现。现有方法虽能探索中间步骤以生成计划，但依赖不可靠的自我验证或外部验证器来评估这些步骤，需大量数据和计算资源。本文提出自动化启发式发现（AutoHD），使LLM能在推理时显式生成启发式函数，用于准确评估中间状态。这些启发式函数通过启发式演化过程进一步优化，提升其鲁棒性和有效性。该方法无需额外模型训练或微调，且由LLM生成的显式启发式函数增强了推理过程的可解释性。在多个基准测试上的广泛实验表明，本方法显著优于多个基线，在某些数据集上准确率接近翻倍，证明了其在复杂规划任务中可靠且可解释的优势。
原文摘要 · Abstract (English)
We consider enhancing large language models (LLMs) for complex planning tasks. While existing methods allow LLMs to explore intermediate steps to make plans, they either depend on unreliable self-verification or external verifiers to evaluate these steps, which demand significant data and computations. Here, we propose automated heuristics discovery (AutoHD), a novel approach that enables LLMs to explicitly generate heuristic functions to guide inference-time search, allowing accurate evaluation of intermediate states. These heuristic functions are further refined through a heuristic evolution process, improving their robustness and effectiveness. Our proposed method requires no additional model training or fine-tuning, and the explicit definition of heuristic functions generated by the LLMs provides interpretability and insights into the reasoning process. Extensive experiments across diverse benchmarks demonstrate significant gains over multiple baselines, including nearly twice the accuracy on some datasets, establishing our approach as a reliable and interpretable solution for complex planning tasks.
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