@inproceedings{lorenzo-etal-2025-translating,
    title = "Translating Tax Law to Code with {LLM}s: A Benchmark and Evaluation Framework",
    author = "Lorenzo, Gabriele  and
      Pietromatera, Aldo  and
      Holzenberger, Nils",
    editor = "Aletras, Nikolaos  and
      Chalkidis, Ilias  and
      Barrett, Leslie  and
      Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina  and
      Preoțiuc-Pietro, Daniel  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.nllp-1.4/",
    pages = "31--47",
    ISBN = "979-8-89176-338-8",
    abstract = "Catala is a domain-specific programming language for tax law, meant to facilitate the translation of legal text into executable computer code, thanks to a syntax close to that of legal language and reasoning. Legal statutes paired with their Catala translation have been published online periodically, but manual translation remains labor-intensive. In this work, we develop a benchmark for the evaluation of Catala code generation from legal text, including a training set to fine-tune Large Language Models. To assess the quality of the generated code, we introduce an evaluation framework extending current metrics for code generation. Our experiments with few-shot learning, as well as fine-tuned models, suggest the feasibility of automating legal code generation, and contrast with prior attempts to translate legal language into a formal representation."
}