用大模型当启发式，让规划更准更可执行
Large Language Models as Common-Sense Heuristics
- 让大模型输出作为爬山搜索的启发信息
- 在家庭环境中成功率提升22个百分点
- 无需中间语言，直接用原始动作表示
尽管专门用于规划任务的系统性能远超大语言模型（LLMs），但通常会忽略任务描述中的丰富语义信息。相比之下，LLMs 拥有广泛领域的参数化知识，能利用自然语言描述来生成解决方案。然而，现有研究在生成正确且可执行计划方面仍面临挑战，且依赖 LLM 输出中间语言，需翻译为任务的表示语言。本文提出一种新规划方法：利用 LLM 输出作为爬山搜索的启发式，并通过提示生成解的估计值引导搜索。该方法在常见家庭环境中使任务成功率比同类系统高出22个百分点，且计划始终可执行。所有动作均以原始表示编码，无需翻译步骤，证明强性能可在无中间语言的情况下实现。
原文摘要 · Abstract (English)
While systems designed for solving planning tasks vastly outperform Large Language Models (LLMs) in this domain, they usually discard the rich semantic information embedded within task descriptions. In contrast, LLMs possess parametrised knowledge across a wide range of topics, enabling them to leverage the natural language descriptions of planning tasks in their solutions. However, current research in this direction faces challenges in generating correct and executable plans. Furthermore, these approaches depend on the LLM to output solutions in an intermediate language, which must be translated into the representation language of the planning task. We introduce a novel planning method, which leverages the parametrised knowledge of LLMs by using their output as a heuristic for Hill-Climbing Search. This approach is further enhanced by prompting the LLM to generate a solution estimate to guide the search. Our method outperforms the task success rate of similar systems within a common household environment by 22 percentage points, with consistently executable plans. All actions are encoded in their original representation, demonstrating that strong results can be achieved without an intermediate language, thus eliminating the need for a translation step.
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