Complex LLM Planning via Automated Heuristics Discovery

• URL: http://arxiv.org/abs/2502.19295v1
• Date: Wed, 26 Feb 2025 16:52:31 GMT
• Title: Complex LLM Planning via Automated Heuristics Discovery
• Authors: Hongyi Ling, Shubham Parashar, Sambhav Khurana, Blake Olson, Anwesha Basu, Gaurangi Sinha, Zhengzhong Tu, James Caverlee, Shuiwang Ji,
• Abstract summary: We consider enhancing large language models (LLMs) for complex planning tasks.<n>We propose automated inferences discovery (AutoHD), a novel approach that enables LLMs to explicitly generate functions to guide-time search.<n>Our proposed method requires no additional model training or finetuning--and the explicit definition of functions generated by the LLMs provides interpretability and insights into the reasoning process.
• Score: 48.07520536415374
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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