Using LLMs to Discover Legal Factors

• URL: http://arxiv.org/abs/2410.07504v1
• Date: Thu, 10 Oct 2024 00:42:10 GMT
• Title: Using LLMs to Discover Legal Factors
• Authors: Morgan Gray, Jaromir Savelka, Wesley Oliver, Kevin Ashley,
• Abstract summary: We use large language models to discover factors that effectively represent a legal domain. Our method takes as input raw court opinions and produces a set of factors and associated definitions. We demonstrate that a semi-automated approach, incorporating minimal human involvement, produces factor representations that can predict case outcomes with moderate success.
• Score: 0.6249768559720122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Factors are a foundational component of legal analysis and computational models of legal reasoning. These factor-based representations enable lawyers, judges, and AI and Law researchers to reason about legal cases. In this paper, we introduce a methodology that leverages large language models (LLMs) to discover lists of factors that effectively represent a legal domain. Our method takes as input raw court opinions and produces a set of factors and associated definitions. We demonstrate that a semi-automated approach, incorporating minimal human involvement, produces factor representations that can predict case outcomes with moderate success, if not yet as well as expert-defined factors can.

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