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            <title>Proceedings of the Natural Legal Language Processing Workshop 2025</title>
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            <namePart type="family">Aletras</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Translating Tax Law to Code with LLMs: A Benchmark and Evaluation Framework
%A Lorenzo, Gabriele
%A Pietromatera, Aldo
%A Holzenberger, Nils
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-338-8
%F lorenzo-etal-2025-translating
%X 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.
%U https://aclanthology.org/2025.nllp-1.4/
%P 31-47

Markdown (Informal)

[Translating Tax Law to Code with LLMs: A Benchmark and Evaluation Framework](https://aclanthology.org/2025.nllp-1.4/) (Lorenzo et al., NLLP 2025)

• Translating Tax Law to Code with LLMs: A Benchmark and Evaluation Framework (Lorenzo et al., NLLP 2025)

ACL

• Gabriele Lorenzo, Aldo Pietromatera, and Nils Holzenberger. 2025. Translating Tax Law to Code with LLMs: A Benchmark and Evaluation Framework. In Proceedings of the Natural Legal Language Processing Workshop 2025, pages 31–47, Suzhou, China. Association for Computational Linguistics.