让大模型借助求解器提示，提升复杂路径规划能力
Can LLMs plan paths with extra hints from solvers?
- 引入求解器生成反馈，辅助大模型进行长程规划
- 中等难度问题成功率显著提升，难题仍难以解决
- 适合研究大模型推理与外部工具协同的学者
大语言模型（LLMs）在自然语言处理、数学求解和程序合成任务中表现突出，但在长期规划和高阶推理方面仍显局限且脆弱。本文探索通过整合求解器生成的反馈来增强LLM在经典机器人路径规划任务中的表现。研究对比了四种不同的反馈策略，包括视觉反馈，并采用微调方法。在10个标准和100个随机生成的规划问题上评估了三种不同LLM的表现。结果表明，求解器反馈能有效提升模型对中等难度问题的求解能力，但更困难的问题仍难以攻克。研究还深入分析了不同提示策略的影响及各模型的规划倾向差异。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown remarkable capabilities in natural language processing, mathematical problem solving, and tasks related to program synthesis. However, their effectiveness in long-term planning and higher-order reasoning has been noted to be limited and fragile. This paper explores an approach for enhancing LLM performance in solving a classical robotic planning task by integrating solver-generated feedback. We explore four different strategies for providing feedback, including visual feedback, we utilize fine-tuning, and we evaluate the performance of three different LLMs across a 10 standard and 100 more randomly generated planning problems. Our results suggest that the solver-generated feedback improves the LLM's ability to solve the moderately difficult problems, but the harder problems still remain out of reach. The study provides detailed analysis of the effects of the different hinting strategies and the different planning tendencies of the evaluated LLMs.
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