@inproceedings{al-laith-agirrezabal-2026-legal,
title = "From Legal Text to Executable Process: {LLM}-Based Extraction of {DCR} Determinations from {D}anish Administrative Law",
author = "Al-Laith, Ali and
Agirrezabal, Manex",
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 and
Xu, Shanshan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nllp-1.50/",
pages = "640--640",
ISBN = "979-8-89176-494-1",
abstract = "Formal representations of legal text can ground conversational legal assistants in structured, verifiable knowledge rather than relying solely on a language models latent understanding. A key bottleneck in building such representations is extracting the underlying legal ``determinations'' from natural language documents. We investigate whether large language models can perform this extraction directly via prompting. We prompt six open-weight models to extract determinations from chunked sections of the document, and evaluate their output against a manually built gold standard using both character-level and semantic metrics, in both gold-to-predicted and predicted-to-gold directions, alongside a novel pseudo-precision/recall formulation. Results show that models vary substantially in schema conformance, output length, and number of determinations retrieved, with no single model dominating across all metrics: some achieve stronger semantic alignment with gold determinations, while others better preserve one-to-one correspondence in the reverse direction."
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<abstract>Formal representations of legal text can ground conversational legal assistants in structured, verifiable knowledge rather than relying solely on a language models latent understanding. A key bottleneck in building such representations is extracting the underlying legal “determinations” from natural language documents. We investigate whether large language models can perform this extraction directly via prompting. We prompt six open-weight models to extract determinations from chunked sections of the document, and evaluate their output against a manually built gold standard using both character-level and semantic metrics, in both gold-to-predicted and predicted-to-gold directions, alongside a novel pseudo-precision/recall formulation. Results show that models vary substantially in schema conformance, output length, and number of determinations retrieved, with no single model dominating across all metrics: some achieve stronger semantic alignment with gold determinations, while others better preserve one-to-one correspondence in the reverse direction.</abstract>
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%0 Conference Proceedings
%T From Legal Text to Executable Process: LLM-Based Extraction of DCR Determinations from Danish Administrative Law
%A Al-Laith, Ali
%A Agirrezabal, Manex
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F al-laith-agirrezabal-2026-legal
%X Formal representations of legal text can ground conversational legal assistants in structured, verifiable knowledge rather than relying solely on a language models latent understanding. A key bottleneck in building such representations is extracting the underlying legal “determinations” from natural language documents. We investigate whether large language models can perform this extraction directly via prompting. We prompt six open-weight models to extract determinations from chunked sections of the document, and evaluate their output against a manually built gold standard using both character-level and semantic metrics, in both gold-to-predicted and predicted-to-gold directions, alongside a novel pseudo-precision/recall formulation. Results show that models vary substantially in schema conformance, output length, and number of determinations retrieved, with no single model dominating across all metrics: some achieve stronger semantic alignment with gold determinations, while others better preserve one-to-one correspondence in the reverse direction.
%U https://aclanthology.org/2026.nllp-1.50/
%P 640-640
Markdown (Informal)
[From Legal Text to Executable Process: LLM-Based Extraction of DCR Determinations from Danish Administrative Law](https://aclanthology.org/2026.nllp-1.50/) (Al-Laith & Agirrezabal, NLLP 2026)
ACL