用大模型从判例中自动提炼法律因素，辅助法律推理。
Using LLMs to Discover Legal Factors
- 输入法院判例，用大模型自动提取法律因素及定义。
- 半自动化方法可中等程度预测案件结果，接近专家水平。
- 适合法律AI研究者和司法智能化探索者参考。
因素是法律分析和法律推理计算模型的基础组成部分。基于因素的表示方法使律师、法官以及AI与法律研究者能够对法律案件进行推理。本文提出一种利用大语言模型（LLMs）发现有效代表法律领域的因素列表的方法。该方法以原始法院判例为输入，生成一组因素及其相关定义。我们证明，结合少量人工干预的半自动化流程，能生成可中等程度预测案件结果的因素表示，虽尚未达到专家定义因素的性能，但已具备实用潜力。
原文摘要 · Abstract (English)
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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