Beyond the Bar: Generative AI as a Transformative Component in Legal Document Review

Review for responsiveness is a recall-oriented document classification task central to civil litigation. In large legal matters, it may involve the coding of millions of documents by teams of dozens to hundreds of contract attorneys. We describe a prototype document review system based on a large language model (LLM) for replacing the first level of attorney review. Our system accepts the same guidance—a written review protocol—that would be provided to a human review team. We tested our prototype in the context of a live legal matter, evaluating both human review and our LLM-based system against a gold standard coded by expert senior attorneys. Our prototype achieved an estimated 96% recall and 60% precision without matter-specific tuning, and has numerous avenues for further improvement.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10826089
Citations
1
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
2.23
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article

Beyond the Bar: Generative AI as a Transformative Component in Legal Document Review

Lenora Gray, David Lewis, Reza Omrani, Jeremy Pickens et al.
1 citations
Artificial Intelligence in Law
2.23
article

Beyond the Bar: Generative AI as a Transformative Component in Legal Document Review

Lenora Gray, David Lewis, Reza Omrani, Jeremy Pickens, S.A. Underwood, Eugene Yang, Aron J. Ahmadia, Evan Curtin, Tara Emory, Nathan Reff, Cristin Traylor
article en
1 citations

Abstract

Review for responsiveness is a recall-oriented document classification task central to civil litigation. In large legal matters, it may involve the coding of millions of documents by teams of dozens to hundreds of contract attorneys. We describe a prototype document review system based on a large language model (LLM) for replacing the first level of attorney review. Our system accepts the same guidance—a written review protocol—that would be provided to a human review team. We tested our prototype in the context of a live legal matter, evaluating both human review and our LLM-based system against a gold standard coded by expert senior attorneys. Our prototype achieved an estimated 96% recall and 60% precision without matter-specific tuning, and has numerous avenues for further improvement.

Johns Hopkins University (US), Covington and Burling (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Artificial Intelligence in Law
2.23
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Beyond the Bar: Generative AI as a Transformative Component in Legal Document Review — Lenora Gray, David Lewis, et al. · (2024) | TGRS Research Map | TGRS