@inproceedings{huang-zhang-2025-limit,
title = "On the Limit of Language Models as Planning Formalizers",
author = "Huang, Cassie and
Zhang, Li",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.242/",
doi = "10.18653/v1/2025.acl-long.242",
pages = "4880--4904",
ISBN = "979-8-89176-251-0",
abstract = "Large Language Models have been found to create plans that are neither executable nor verifiable in grounded environments. An emerging line of work demonstrates success in using the LLM as a formalizer to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). This formal representation can be deterministically solved to find a plan. We systematically evaluate this methodology while bridging some major gaps. While previous work only generates a partial PDDL representation, given templated, and therefore unrealistic environment descriptions, we generate the complete representation given descriptions of various naturalness levels. Among an array of observations critical to improve LLMs' formal planning abilities, we note that most large enough models can effectively formalize descriptions as PDDL, outperforming those directly generating plans, while being robust to lexical perturbation. As the descriptions become more natural-sounding, we observe a decrease in performance and provide detailed error analysis."
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<abstract>Large Language Models have been found to create plans that are neither executable nor verifiable in grounded environments. An emerging line of work demonstrates success in using the LLM as a formalizer to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). This formal representation can be deterministically solved to find a plan. We systematically evaluate this methodology while bridging some major gaps. While previous work only generates a partial PDDL representation, given templated, and therefore unrealistic environment descriptions, we generate the complete representation given descriptions of various naturalness levels. Among an array of observations critical to improve LLMs’ formal planning abilities, we note that most large enough models can effectively formalize descriptions as PDDL, outperforming those directly generating plans, while being robust to lexical perturbation. As the descriptions become more natural-sounding, we observe a decrease in performance and provide detailed error analysis.</abstract>
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%0 Conference Proceedings
%T On the Limit of Language Models as Planning Formalizers
%A Huang, Cassie
%A Zhang, Li
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F huang-zhang-2025-limit
%X Large Language Models have been found to create plans that are neither executable nor verifiable in grounded environments. An emerging line of work demonstrates success in using the LLM as a formalizer to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). This formal representation can be deterministically solved to find a plan. We systematically evaluate this methodology while bridging some major gaps. While previous work only generates a partial PDDL representation, given templated, and therefore unrealistic environment descriptions, we generate the complete representation given descriptions of various naturalness levels. Among an array of observations critical to improve LLMs’ formal planning abilities, we note that most large enough models can effectively formalize descriptions as PDDL, outperforming those directly generating plans, while being robust to lexical perturbation. As the descriptions become more natural-sounding, we observe a decrease in performance and provide detailed error analysis.
%R 10.18653/v1/2025.acl-long.242
%U https://aclanthology.org/2025.acl-long.242/
%U https://doi.org/10.18653/v1/2025.acl-long.242
%P 4880-4904
Markdown (Informal)
[On the Limit of Language Models as Planning Formalizers](https://aclanthology.org/2025.acl-long.242/) (Huang & Zhang, ACL 2025)
ACL
- Cassie Huang and Li Zhang. 2025. On the Limit of Language Models as Planning Formalizers. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4880–4904, Vienna, Austria. Association for Computational Linguistics.