BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law
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Computer Science > Computation and Language
Title:BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law
Abstract:We introduce the BenGER (Benchmark for German Law) dataset for evaluating LLM systems on subsumption-based legal reasoning in German law. The BenGER dataset consists of three components: 596 exam-style free-text legal case tasks across multiple levels of legal education and 531 short doctrinal reasoning tasks. We evaluate 12 contemporary LLM systems -- closed flagship, efficiency-oriented, and open-weight -- across automatic and judge-based metrics. On a controlled validation subset of timed human-written solutions under both unaided and human--AI co-creation conditions, we contextualise model performance against these human baselines. We introduce a rubric-aligned LLM-as-a-Judge framework cross-validated against a multi-rater human-grading protocol (three blind reviews plus one author-informed creator review per solution). Our results show that replacing a blind human reviewer with the LLM judge degrades agreement with the full human pool no more than removing that reviewer altogether (Calderon r=0.96 vs.~r=0.96, matched n=30), that closed-flagship systems lead the leaderboard across all corpora, and that human--AI co-creation substantially outperforms unaided human work.
| Comments: | Pre-Print |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.28183 [cs.CL] |
| (or arXiv:2605.28183v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28183
arXiv-issued DOI via DataCite (pending registration)
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