@inproceedings{rabbani-etal-2026-decode,
title = "``Decode the Law'': Towards Legal Text Simplification with Large Language Models",
author = "Rabbani, Mohammed Danish and
Roy, Subhadeep and
Mitra, Sayantan and
Saha, Tulika",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.45/",
doi = "10.63317/2i7en3xycmhi",
pages = "631--641",
abstract = "Legal documents are often verbose and structurally complex, posing significant barriers to public understanding and equitable access to justice. Despite growing interest in text simplification, efforts targeting the legal domain remain limited by a lack of robust, high-quality resources. In this paper, we address this gap by introducing SIMPLE-LAW, a curated benchmark dataset of over 6,000 aligned pairs of original and simplified legal passages, specifically constructed to facilitate research in legal text simplification by leveraging large language models (LLMs). We evaluate this dataset across both in-context learning and parameter-efficient fine-tuning paradigms using a range of state-of-the-art LLMs, with Unsloth variants of Mistral, LLaMA-3.2, Gemma, and Qwen-2.5. We assess performance using BERTScore, ROUGE, SARI, and a hallucination detection score, to capture both fidelity and readability. Results show that fine-tuned models significantly outperform in-context learners in terms of simplification quality and factual consistency. By offering a new dataset, rigorous evaluation, and baseline comparisons, our work provides a critical foundation for developing transparent and accessible AI systems in the legal domain."
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<abstract>Legal documents are often verbose and structurally complex, posing significant barriers to public understanding and equitable access to justice. Despite growing interest in text simplification, efforts targeting the legal domain remain limited by a lack of robust, high-quality resources. In this paper, we address this gap by introducing SIMPLE-LAW, a curated benchmark dataset of over 6,000 aligned pairs of original and simplified legal passages, specifically constructed to facilitate research in legal text simplification by leveraging large language models (LLMs). We evaluate this dataset across both in-context learning and parameter-efficient fine-tuning paradigms using a range of state-of-the-art LLMs, with Unsloth variants of Mistral, LLaMA-3.2, Gemma, and Qwen-2.5. We assess performance using BERTScore, ROUGE, SARI, and a hallucination detection score, to capture both fidelity and readability. Results show that fine-tuned models significantly outperform in-context learners in terms of simplification quality and factual consistency. By offering a new dataset, rigorous evaluation, and baseline comparisons, our work provides a critical foundation for developing transparent and accessible AI systems in the legal domain.</abstract>
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%0 Conference Proceedings
%T “Decode the Law”: Towards Legal Text Simplification with Large Language Models
%A Rabbani, Mohammed Danish
%A Roy, Subhadeep
%A Mitra, Sayantan
%A Saha, Tulika
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F rabbani-etal-2026-decode
%X Legal documents are often verbose and structurally complex, posing significant barriers to public understanding and equitable access to justice. Despite growing interest in text simplification, efforts targeting the legal domain remain limited by a lack of robust, high-quality resources. In this paper, we address this gap by introducing SIMPLE-LAW, a curated benchmark dataset of over 6,000 aligned pairs of original and simplified legal passages, specifically constructed to facilitate research in legal text simplification by leveraging large language models (LLMs). We evaluate this dataset across both in-context learning and parameter-efficient fine-tuning paradigms using a range of state-of-the-art LLMs, with Unsloth variants of Mistral, LLaMA-3.2, Gemma, and Qwen-2.5. We assess performance using BERTScore, ROUGE, SARI, and a hallucination detection score, to capture both fidelity and readability. Results show that fine-tuned models significantly outperform in-context learners in terms of simplification quality and factual consistency. By offering a new dataset, rigorous evaluation, and baseline comparisons, our work provides a critical foundation for developing transparent and accessible AI systems in the legal domain.
%R 10.63317/2i7en3xycmhi
%U https://aclanthology.org/2026.lrec-1.45/
%U https://doi.org/10.63317/2i7en3xycmhi
%P 631-641
Markdown (Informal)
[“Decode the Law": Towards Legal Text Simplification with Large Language Models](https://aclanthology.org/2026.lrec-1.45/) (Rabbani et al., LREC 2026)
ACL