arXiv Open Access 2024

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

Haitao Li You Chen Qingyao Ai Yueyue Wu Ruizhe Zhang +1 lainnya
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Abstrak

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.

Topik & Kata Kunci

Penulis (6)

H

Haitao Li

Y

You Chen

Q

Qingyao Ai

Y

Yueyue Wu

R

Ruizhe Zhang

Y

Yiqun Liu

Format Sitasi

Li, H., Chen, Y., Ai, Q., Wu, Y., Zhang, R., Liu, Y. (2024). LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models. https://arxiv.org/abs/2409.20288

Akses Cepat

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Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓