用AI解析美国版权赔偿判决中的法官推理逻辑，揭示判罚差异根源
Dissecting Judicial Reasoning in U.S. Copyright Damage Awards
- 基于修辞结构理论构建LLM分析框架，拆解判决书的论证层级
- 发现不同司法辖区对赔偿因素的权重差异显著，传统方法难以捕捉
- 适合法律科技开发者、法理研究者及版权政策制定者参考
美国版权赔偿判决中的司法推理存在核心挑战，尽管联邦法院遵循1976年《版权法》，但各地对法律条文的解释和因素权重分配差异巨大，导致诉讼结果不可预测，削弱了法律决策的实证基础。本研究提出一种新型基于话语的大型语言模型（LLM）方法，融合修辞结构理论（RST）与代理工作流，从司法意见中提取并量化以往隐晦的推理模式。该框架通过三阶段流程——数据集构建、话语分析与代理特征提取——将判决文本解析为层次化话语结构，识别推理组件并提取对应的话语子树标签。在分析版权赔偿裁决时，发现话语增强型LLM分析优于传统方法，并揭示出各巡回法院在因素权重上存在未被量化的差异。研究成果既推动了计算法律分析的方法创新，也为法律从业者、研究者及政策制定者提供了实践洞见。
原文摘要 · Abstract (English)
Judicial reasoning in copyright damage awards poses a core challenge for computational legal analysis. Although federal courts follow the 1976 Copyright Act, their interpretations and factor weightings vary widely across jurisdictions. This inconsistency creates unpredictability for litigants and obscures the empirical basis of legal decisions. This research introduces a novel discourse-based Large Language Model (LLM) methodology that integrates Rhetorical Structure Theory (RST) with an agentic workflow to extract and quantify previously opaque reasoning patterns from judicial opinions. Our framework addresses a major gap in empirical legal scholarship by parsing opinions into hierarchical discourse structures and using a three-stage pipeline, i.e., Dataset Construction, Discourse Analysis, and Agentic Feature Extraction. This pipeline identifies reasoning components and extract feature labels with corresponding discourse subtrees. In analyzing copyright damage rulings, we show that discourse-augmented LLM analysis outperforms traditional methods while uncovering unquantified variations in factor weighting across circuits. These findings offer both methodological advances in computational legal analysis and practical insights into judicial reasoning, with implications for legal practitioners seeking predictive tools, scholars studying legal principle application, and policymakers confronting inconsistencies in copyright law.
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