aiqbench analysis
BenGER establishes a rigorous new standard for evaluating large language models on complex, subsumption-based legal reasoning within the German jurisdiction. The study confirms that closed-flagship models currently outperform open-weight alternatives while demonstrating that AI-human co-creation yields measurable improvements over unaided human performance in this specialized domain. This benchmark provides critical data points for researchers assessing the reliability and alignment of LLMs in high-stakes professional applications.
BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law
arXiv:2605.28183v4 Announce Type: replace-cross Abstract: We introduce BenGER (Benchmark for German Law), a benchmark and dataset for evaluating LLM systems on subsumption-based legal reasoning in German law. The dataset combines 596 exam-style free-text legal case tasks across multiple levels of legal education and 531 short doctrinal reasoning tasks. It includes a controlled validation subset of timed human-written solutions under both unaided and human-AI co-creation conditions. We evaluate 12 contemporary LLM systems - closed flagship, efficiency-oriented, and open-weight - with a rubric-aligned LLM-as-a-Judge cross-validated against a multi-rater human-grading layer (three blind reviews per solution, six judge families benchmarked against the human pool). Closed-flagship systems lead the leaderboard across all three corpora, human-AI co-creation measurably improves on unaided human work, and the LLM judge tracks human grading at Pearson r=0.76 and Cohen's k=0.60. System rankings are stable across judge families and two judges from independent providers clear the Calderon single-reviewer replacement bar on human-authored solutions.
Abstract:We introduce BenGER (Benchmark for German Law), a benchmark and dataset for evaluating LLM systems on subsumption-based legal reasoning in German law. The dataset combines 596 exam-style free-text legal case tasks across multiple levels of legal education and 531 short doctrinal reasoning tasks. It includes a controlled validation subset of timed human-written solutions under both unaided and human-AI co-creation conditions. We evaluate 12 contemporary LLM systems - closed flagship, efficiency-oriented, and open-weight - with a rubric-aligned LLM-as-a-Judge cross-validated against a multi-rater human-grading layer (three blind reviews per solution, six judge families benchmarked against the human pool). Closed-flagship systems lead the leaderboard across all three corpora, human-AI co-creation measurably improves on unaided human work, and the LLM judge tracks human grading at Pearson r=0.76 and Cohen's k=0.60. System rankings are stable across judge families and two judges from independent providers clear the Calderon single-reviewer replacement bar on human-authored solutions.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2605.28183 [cs.CL]
(or arXiv:2605.28183v4 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.28183 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 244 words
