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LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models

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arxiv 2409.20288 v4 pith:XCUX2646 submitted 2024-09-30 cs.CL

classification cs.CL
keywords legallexevalllmschinesebenchmarkdatasetsevaluationlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) have made significant progress in natural language processing tasks and demonstrate considerable potential in the legal domain. However, legal applications demand high standards of accuracy, reliability, and fairness. Applying existing LLMs to legal systems without careful evaluation of their potential and limitations could pose significant risks in legal practice. To this end, we introduce a standardized comprehensive Chinese legal benchmark LexEval. This benchmark is notable in the following three aspects: (1) Ability Modeling: We propose a new taxonomy of legal cognitive abilities to organize different tasks. (2) Scale: To our knowledge, LexEval is currently the largest Chinese legal evaluation dataset, comprising 23 tasks and 14,150 questions. (3) Data: we utilize formatted existing datasets, exam datasets and newly annotated datasets by legal experts to comprehensively evaluate the various capabilities of LLMs. LexEval not only focuses on the ability of LLMs to apply fundamental legal knowledge but also dedicates efforts to examining the ethical issues involved in their application. We evaluated 38 open-source and commercial LLMs and obtained some interesting findings. The experiments and findings offer valuable insights into the challenges and potential solutions for developing Chinese legal systems and LLM evaluation pipelines. The LexEval dataset and leaderboard are publicly available at \url{https://github.com/CSHaitao/LexEval} and will be continuously updated.

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Cited by 3 Pith papers

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    cs.CL 2026-07 conditional novelty 7.0 of 10

    BLAD releases 1,484 Bangladeshi legal acts (1799–2025) with structural annotations and historical government context.

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    An automated LLM-based evaluator finds that eight LLMs rarely hallucinate factors in legal argument generation but often omit relevant factors and usually fail to abstain when no common ground exists.

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    A comprehensive survey of the bidirectional links between LLMs and data management, organized as DATA4LLM and LLM4DATA with a new 'IaaS' data-quality framework.

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