@inproceedings{watson-etal-2025-law,
title = "{LAW}: Legal Agentic Workflows for Custody and Fund Services Contracts",
author = "Watson, William and
Cho, Nicole and
Srishankar, Nishan and
Zeng, Zhen and
Cecchi, Lucas and
Scott, Daniel and
Siddagangappa, Suchetha and
Kaur, Rachneet and
Balch, Tucker and
Veloso, Manuela",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.50/",
pages = "583--594",
abstract = "Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is challenging for an off-the-shelf Large Language Model (LLM) to ingest these contracts due to the lengthy unstructured streams of text, limited LLM context windows, and complex legal jargon. To address these challenges, we introduce LAW (Legal Agentic Workflows for Custody and Fund Services Contracts). LAW features a modular design that responds to user queries by orchestrating a suite of domain-specific tools and text agents. Our experiments demonstrate that LAW, by integrating multiple specialized agents and tools, significantly outperforms the baseline. LAW excels particularly in complex tasks such as calculating a contract`s termination date, surpassing the baseline by 92.9{\%} points. Furthermore, LAW offers a cost-effective alternative to traditional fine-tuned legal LLMs by leveraging reusable, domain-specific tools."
}
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<abstract>Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is challenging for an off-the-shelf Large Language Model (LLM) to ingest these contracts due to the lengthy unstructured streams of text, limited LLM context windows, and complex legal jargon. To address these challenges, we introduce LAW (Legal Agentic Workflows for Custody and Fund Services Contracts). LAW features a modular design that responds to user queries by orchestrating a suite of domain-specific tools and text agents. Our experiments demonstrate that LAW, by integrating multiple specialized agents and tools, significantly outperforms the baseline. LAW excels particularly in complex tasks such as calculating a contract‘s termination date, surpassing the baseline by 92.9% points. Furthermore, LAW offers a cost-effective alternative to traditional fine-tuned legal LLMs by leveraging reusable, domain-specific tools.</abstract>
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<date>2025-01</date>
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%0 Conference Proceedings
%T LAW: Legal Agentic Workflows for Custody and Fund Services Contracts
%A Watson, William
%A Cho, Nicole
%A Srishankar, Nishan
%A Zeng, Zhen
%A Cecchi, Lucas
%A Scott, Daniel
%A Siddagangappa, Suchetha
%A Kaur, Rachneet
%A Balch, Tucker
%A Veloso, Manuela
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F watson-etal-2025-law
%X Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is challenging for an off-the-shelf Large Language Model (LLM) to ingest these contracts due to the lengthy unstructured streams of text, limited LLM context windows, and complex legal jargon. To address these challenges, we introduce LAW (Legal Agentic Workflows for Custody and Fund Services Contracts). LAW features a modular design that responds to user queries by orchestrating a suite of domain-specific tools and text agents. Our experiments demonstrate that LAW, by integrating multiple specialized agents and tools, significantly outperforms the baseline. LAW excels particularly in complex tasks such as calculating a contract‘s termination date, surpassing the baseline by 92.9% points. Furthermore, LAW offers a cost-effective alternative to traditional fine-tuned legal LLMs by leveraging reusable, domain-specific tools.
%U https://aclanthology.org/2025.coling-industry.50/
%P 583-594
Markdown (Informal)
[LAW: Legal Agentic Workflows for Custody and Fund Services Contracts](https://aclanthology.org/2025.coling-industry.50/) (Watson et al., COLING 2025)
ACL
- William Watson, Nicole Cho, Nishan Srishankar, Zhen Zeng, Lucas Cecchi, Daniel Scott, Suchetha Siddagangappa, Rachneet Kaur, Tucker Balch, and Manuela Veloso. 2025. LAW: Legal Agentic Workflows for Custody and Fund Services Contracts. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 583–594, Abu Dhabi, UAE. Association for Computational Linguistics.