用统一框架提升大模型生成规划语言的效率与可靠性
Unifying Inference-Time Planning Language Generation
- 提出基于中间表示的统一框架，整合多种规划语言生成方法
- 在十余个管道上系统评估，证明高资源中间语言更有效
- 适合对可解释性与稳定性要求高的规划系统研究者
一类规划研究不直接让大模型生成计划，而是利用其生成某种形式化规划语言表示，再输入符号求解器以确定性地找到计划。尽管提升了可信度并展现出良好性能，近期数十篇论文提出了分散的方法，在不同基准和实验设置下表现各异。本文尝试通过基于中间表示的统一框架，整合推理时将大模型作为形式化工具的方法。系统评估了十余种涵盖现有主流工作且包含语法相似但资源消耗更高的中间语言（如PDDL的Python封装）的新方案。提供规划语言生成流水线的构建指南，总结各组件的有效性，并验证其对问题复杂度的鲁棒性。
原文摘要 · Abstract (English)
A line of work in planning uses LLM not to generate a plan, but to generate a formal representation in some planning language, which can be input into a symbolic solver to deterministically find a plan. While showing improved trust and promising performance, dozens of recent publications have proposed scattered methods on a variety of benchmarks under different experimental settings. We attempt to unify the inference-time LLM-as-formalizer methodology for classical planning by proposing a unifying framework based on intermediate representations. We thus systematically evaluate more than a dozen pipelines that subsume most existing work, while proposing novel ones that involve syntactically similar but high resource intermediate languages (such as a Python wrapper of PDDL). We provide recipes for planning language generation pipelines, draw a series of conclusions showing the efficacy of their various components, and evidence their robustness against problem complexity.
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