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            <title>Proceedings of the Natural Legal Language Processing Workshop 2026</title>
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            <namePart type="given">Nikolaos</namePart>
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    <abstract>Long-form criminal-law generation requires factual interpretation, issue selection, and structured formulation of legal conclusions across offense elements and general doctrines. But existing neuro-symbolic approaches encode only task-specific decision rules, not a reusable representation of criminal-law structure. We introduce CLARE, a domain-specific language for reusable criminal-law structure, which we instantiate with Korean criminal law. CLARE compiles definitions of each unit offense into an executable legal program that constrains case-time legal reasoning. The resulting program then composes case-level judgments before long-form generation. We evaluate CLARE on long-form generation and component-level Korean legal benchmarks, showing that symbolic composition improves reliability over domain-specific baselines. Our results suggest that explicitly representing criminal-law structure provides a practical foundation for reliable neuro-symbolic long-form legal generation.</abstract>
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%0 Conference Proceedings
%T CLARE: A Criminal-Law DSL for Neuro-Symbolic Long-Form Legal Reasoning
%A Jeong, Jaehoon
%A Lee, Jay-Yoon
%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 jeong-lee-2026-clare
%X Long-form criminal-law generation requires factual interpretation, issue selection, and structured formulation of legal conclusions across offense elements and general doctrines. But existing neuro-symbolic approaches encode only task-specific decision rules, not a reusable representation of criminal-law structure. We introduce CLARE, a domain-specific language for reusable criminal-law structure, which we instantiate with Korean criminal law. CLARE compiles definitions of each unit offense into an executable legal program that constrains case-time legal reasoning. The resulting program then composes case-level judgments before long-form generation. We evaluate CLARE on long-form generation and component-level Korean legal benchmarks, showing that symbolic composition improves reliability over domain-specific baselines. Our results suggest that explicitly representing criminal-law structure provides a practical foundation for reliable neuro-symbolic long-form legal generation.
%U https://aclanthology.org/2026.nllp-1.27/
%P 321-321

Markdown (Informal)

[CLARE: A Criminal-Law DSL for Neuro-Symbolic Long-Form Legal Reasoning](https://aclanthology.org/2026.nllp-1.27/) (Jeong & Lee, NLLP 2026)

• CLARE: A Criminal-Law DSL for Neuro-Symbolic Long-Form Legal Reasoning (Jeong & Lee, NLLP 2026)

ACL

• Jaehoon Jeong and Jay-Yoon Lee. 2026. CLARE: A Criminal-Law DSL for Neuro-Symbolic Long-Form Legal Reasoning. In Proceedings of the Natural Legal Language Processing Workshop 2026, pages 321–321, Budapest, Hungary. Association for Computational Linguistics.