@inproceedings{zuo-etal-2025-planetarium,
    title = "Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages",
    author = "Zuo, Max  and
      Velez, Francisco Piedrahita  and
      Li, Xiaochen  and
      Littman, Michael  and
      Bach, Stephen",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.560/",
    pages = "11223--11240",
    ISBN = "979-8-89176-189-6",
    abstract = "Recent works have explored using language models for planning problems. One approach examines translating natural language descriptions of planning tasks into structured planning languages, such as the planning domain definition language (PDDL). Existing evaluation methods struggle to ensure semantic correctness and rely on simple or unrealistic datasets. To bridge this gap, we introduce Planetarium, a benchmark designed to evaluate language models' ability to generate PDDL code from natural language descriptions of planning tasks. Planetarium features a novel PDDL equivalence algorithm that flexibly evaluates the correctness of generated PDDL against ground truth, along with a dataset of 145,918 text-to-PDDL pairs across 73 unique state combinations with varying levels of difficulty. Finally, we evaluate several API-access and open-weight language models that reveal this task{'}s complexity. For example, 96.1{\%} of the PDDL problem descriptions generated by GPT-4o are syntactically parseable, 94.4{\%} are solvable, but only 24.8{\%} are semantically correct, highlighting the need for a more rigorous benchmark for this problem."
}