Leveraging Natural Language Processing and Large Language Models for Assisting Due Diligence in the Legal Domain

Myeongjun Jang, Gábor Stikkel

Abstract
Due diligence is a crucial legal process that mitigates potential risks of mergers and acquisitions (M&A). However, despite its prominent importance, there has been a lack of research regarding leveraging NLP techniques for due diligence. In this study, our aim is to explore the most efficient deep-learning model architecture for due diligence in terms of performance and latency, and evaluate the potential of large language models (LLMs) as an efficient due diligence assistant. To our knowledge, this is the first study that employs pre-trained language models (PLMs) and LLMs for the due diligence problem. Our experimental results suggest that methodologies that have demonstrated promising performance in the general domain encounter challenges when applied in due diligence due to the inherent lengthy nature of legal documents. We also ascertain that LLMs can be a useful tool for helping lawyers who perform due diligence.
Anthology ID:
2024.naacl-industry.14
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Yi Yang, Aida Davani, Avi Sil, Anoop Kumar
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
155–164
Language:
URL:
https://aclanthology.org/2024.naacl-industry.14
DOI:
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Cite (ACL):
Myeongjun Jang and Gábor Stikkel. 2024. Leveraging Natural Language Processing and Large Language Models for Assisting Due Diligence in the Legal Domain. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 155–164, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Leveraging Natural Language Processing and Large Language Models for Assisting Due Diligence in the Legal Domain (Jang & Stikkel, NAACL 2024)
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