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This paper introduces HumRightsBench, the first expert-validated benchmark for evaluating large language models' reasoning about international human rights law. By adapting the IRAC framework to an IRAP structure, the authors create a scalable methodology that incorporates real-world scenarios annotated by human rights professionals. The findings reveal significant variability in model performance across different legal reasoning tasks, indicating the benchmark's potential to enhance AI evaluations in this critical area.
Model accuracy in reasoning about human rights law varies dramatically, with scores ranging from 0.025 to 0.774, highlighting the urgent need for robust evaluation tools in AI.
Large language models (LLMs) increasingly mediate legal determinations over what human rights are realized, and how. Yet, no evaluation benchmark exists to assess whether they can reason correctly about human rights law. To this end, we report our efforts to develop a robust and scalable methodology for creating HumRightsBench: the first expert-validated, scenario-based benchmark for evaluating reasoning grounded in the obligation structure of international human rights law. We adapt the IRAC framework for legal reasoning to better suit the unique reasoning patterns of human rights work (substituting P,"proposing remedies,"for C,"legal conclusion,"yielding IRAP) to structure our evaluation heuristics. We also produce a pilot series of authentic scenarios designed to implicate the many dimensions of real-world human rights issues and annotated by human rights lawyers and professionals across the world. Ultimately, we find that model accuracy scores range considerably across legal reasoning tasks (overall model performance ranges from 0.339 to 0.577, task min-max ranges from 0.025 to 0.774), which strongly implies that HumRightsBench is a capable instrument for advancing this emerging subfield of AI evaluations science at a critical moment in its evolution.
