How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards

2026-09-16

AI Summary

研究针对欧洲人权法院非金钱损害赔偿预测问题，构建了ECtHR-NPD基准，并评估了多种方法，发现复杂模型并未显著优于基于特征的基线模型。

Abstract

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.

Problem

• non-pecuniary damage
• ECtHR
• prediction
• legal benchmark
• monetary remedies

Innovation

• ECtHR-NPD
• non-pecuniary damage awards
• language models
• feature-based baseline
• knowledge-augmented agents