一项人权值多少钱？ECtHR-NPD：预测非金钱损害赔偿的基准
How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards
- University of Sheffield（谢菲尔德大学）
- University of Birmingham（伯明翰大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出首个预测欧洲人权法院非金钱损害赔偿金额的基准ECtHR-NPD，含14,575个案件，评估多种方法发现复杂语言模型未稳定超越特征基线，且难以识别零赔偿和高额赔偿校准，构成当前大模型的挑战性测试平台。
AI中文摘要：
现有的法律基准涵盖了多种任务，而连续性的金钱救济措施相对而言仍未得到充分探索。我们引入了ECtHR-NPD，据我们所知，这是首个从案件信息中预测欧洲人权法院（ECtHR）非金钱损害赔偿（NPD）金额的基准，适用于没有法定公式或明确计算规则决定金额的情形。ECtHR-NPD包含14,575个案件，每个案件有以名义欧元计的案件级赔偿金额、按时间顺序划分的数据分割，以及一个将目标构建与模型输入分离的协议。我们评估了一系列方法，包括常数预测器、梯度提升树、检索方法、微调的编码器语言模型（LM）、提示式解码器LM以及知识增强型智能体。我们的结果表明，更复杂的LM和智能体方法并未持续优于最强的基于特征的基线。所有模型系列都难以识别零赔偿金额，并且在高额赔偿预测的校准上表现不佳，在具有挑战性的测试视图上性能进一步下降，这使得ECtHR-NPD成为当前最先进的开源和专有LM的一个具有挑战性的测试平台。
英文摘要：
Existing legal benchmarks cover diverse tasks, while continuous monetary remedies remain comparatively underexplored. We introduce ECtHR-NPD, to the best of our knowledge, the first benchmark for predicting non-pecuniary damage (NPD) awards at the European Court of Human Rights (ECtHR) from case information when no statutory formula or explicit calculation rule determines the amount. ECtHR-NPD contains 14,575 cases with case-level awards in nominal euros, chronological splits, and a protocol separating target construction from model input. We evaluate a battery of methods, including constant predictors, gradient-boosted trees, retrieval methods, fine-tuned encoder language models (LMs), prompted decoder LMs, and knowledge-augmented agents. Our results show that more sophisticated LM and agentic approaches do not consistently outperform the strongest feature-based baseline. All model families struggle to identify zero awards and to calibrate high-award predictions, with further degradation on the Challenging test view, making ECtHR-NPD a challenging testbed for current state-of-the-art open-weight and proprietary LMs.
