@inproceedings{islam-etal-2025-framework,
title = "A Framework to Retrieve Relevant Laws for Will Execution",
author = "Islam, Md Asiful and
Kwak, Alice Saebom and
Bambauer, Derek and
Morrison, Clayton T and
Surdeanu, Mihai",
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://aclanthology.org/2025.nllp-1.24/",
pages = "338--350",
ISBN = "979-8-89176-338-8",
abstract = "Wills must comply with jurisdiction-specific statutory provisions to be valid, but retrieving the relevant laws for execution, validation, and probate remains labor-intensive and error-prone. Prior legal information retrieval (LIR) research has addressed contracts, criminal law, and judicial decisions, but wills and probate law remain largely unexplored, with no prior work on retrieving statutes for will validity assessment. We propose a legal information retrieval framework that combines lexical and semantic retrieval in a hybrid pipeline with large language model (LLM) reasoning to retrieve the most relevant provisions for a will statement. Evaluations on annotated will-statement datasets from the U.S. states of Tennessee and Idaho using six LLMs show that our hybrid framework consistently outperforms zero-shot baselines. Notably, when paired with our hybrid retrieval pipeline, GPT-5-mini achieves the largest relative accuracy gains, improving by 41.09 points on the Tennessee and 48.68 points on the Idaho test set. We observed similarly strong improvements across all models and datasets."
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<abstract>Wills must comply with jurisdiction-specific statutory provisions to be valid, but retrieving the relevant laws for execution, validation, and probate remains labor-intensive and error-prone. Prior legal information retrieval (LIR) research has addressed contracts, criminal law, and judicial decisions, but wills and probate law remain largely unexplored, with no prior work on retrieving statutes for will validity assessment. We propose a legal information retrieval framework that combines lexical and semantic retrieval in a hybrid pipeline with large language model (LLM) reasoning to retrieve the most relevant provisions for a will statement. Evaluations on annotated will-statement datasets from the U.S. states of Tennessee and Idaho using six LLMs show that our hybrid framework consistently outperforms zero-shot baselines. Notably, when paired with our hybrid retrieval pipeline, GPT-5-mini achieves the largest relative accuracy gains, improving by 41.09 points on the Tennessee and 48.68 points on the Idaho test set. We observed similarly strong improvements across all models and datasets.</abstract>
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%0 Conference Proceedings
%T A Framework to Retrieve Relevant Laws for Will Execution
%A Islam, Md Asiful
%A Kwak, Alice Saebom
%A Bambauer, Derek
%A Morrison, Clayton T.
%A Surdeanu, Mihai
%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 islam-etal-2025-framework
%X Wills must comply with jurisdiction-specific statutory provisions to be valid, but retrieving the relevant laws for execution, validation, and probate remains labor-intensive and error-prone. Prior legal information retrieval (LIR) research has addressed contracts, criminal law, and judicial decisions, but wills and probate law remain largely unexplored, with no prior work on retrieving statutes for will validity assessment. We propose a legal information retrieval framework that combines lexical and semantic retrieval in a hybrid pipeline with large language model (LLM) reasoning to retrieve the most relevant provisions for a will statement. Evaluations on annotated will-statement datasets from the U.S. states of Tennessee and Idaho using six LLMs show that our hybrid framework consistently outperforms zero-shot baselines. Notably, when paired with our hybrid retrieval pipeline, GPT-5-mini achieves the largest relative accuracy gains, improving by 41.09 points on the Tennessee and 48.68 points on the Idaho test set. We observed similarly strong improvements across all models and datasets.
%U https://aclanthology.org/2025.nllp-1.24/
%P 338-350
Markdown (Informal)
[A Framework to Retrieve Relevant Laws for Will Execution](https://aclanthology.org/2025.nllp-1.24/) (Islam et al., NLLP 2025)
ACL
- Md Asiful Islam, Alice Saebom Kwak, Derek Bambauer, Clayton T Morrison, and Mihai Surdeanu. 2025. A Framework to Retrieve Relevant Laws for Will Execution. In Proceedings of the Natural Legal Language Processing Workshop 2025, pages 338–350, Suzhou, China. Association for Computational Linguistics.