用大模型生成分层规划模型，发现效果远不如非分层模型。
Towards a General Framework for HTN Modeling with LLMs
- 扩展L2P工具库，支持大模型生成分层规划模型。
- 在PlanBench上，分层模型解析成功率约36%，语法正确率仅1%
- 揭示大模型生成分层规划的难点，适合规划与LLM交叉研究者参考
大语言模型（LLMs）在自动生成自动化规划（AP）模型方面已广泛应用，但在分层规划（HP）领域的应用仍远未达到非分层架构的成熟水平。本文提出两项主要贡献：首先，推出L2HP，作为L2P（LLM驱动的PDDL模型生成库）的扩展，支持分层规划模型生成，并遵循通用性与可扩展性设计原则；其次，利用该框架在PlanBench数据集上对比了大模型在AP与HP建模中的能力。结果显示，两种场景下的解析成功率相近（约36%），但分层模型的语法正确率显著更低（1% vs. 20%）。这些发现凸显了分层规划对大模型的独特挑战，亟需进一步研究以提升生成分层规划模型的质量。
原文摘要 · Abstract (English)
The use of Large Language Models (LLMs) for generating Automated Planning (AP) models has been widely explored; however, their application to Hierarchical Planning (HP) is still far from reaching the level of sophistication observed in non-hierarchical architectures. In this work, we try to address this gap. We present two main contributions. First, we propose L2HP, an extension of L2P (a library to LLM-driven PDDL models generation) that support HP model generation and follows a design philosophy of generality and extensibility. Second, we apply our framework to perform experiments where we compare the modeling capabilities of LLMs for AP and HP. On the PlanBench dataset, results show that parsing success is limited but comparable in both settings (around 36\%), while syntactic validity is substantially lower in the hierarchical case (1\% vs. 20\% of instances). These findings underscore the unique challenges HP presents for LLMs, highlighting the need for further research to improve the quality of generated HP models.
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