Fei Duan
Author directory2026
ChJGBench: A Comprehensive Benchmark for Chinese Legal Judgment Generation with Diagnostic Evaluation
Yinghao Hu | Linggang Kong | Qian Xiao | Yuting Huang | Yunlong Zhang | Yujie Zhang | Yiquan Wu | Fei Duan | Kaisong Song | Jun Lin | Kun Kuang | Fei Wu
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Yinghao Hu | Linggang Kong | Qian Xiao | Yuting Huang | Yunlong Zhang | Yujie Zhang | Yiquan Wu | Fei Duan | Kaisong Song | Jun Lin | Kun Kuang | Fei Wu
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
With the rapid development of LLMs, increasing attention has been paid to whether they can reliably generate complete and legally valid judicial documents. This calls for a benchmark capable of evaluating whether models can accurately apply statutes and resolve outcome-critical issues, including claims, charges, penalties, and monetary obligations. However, existing benchmarks typically focus on isolated legal tasks or coarse-grained evaluation. We introduce ChJGBench, a benchmark for generating the reasoning and dispositive sections of Chinese judgments across civil, criminal, and administrative cases. Built from 3,462 real-world judgments, ChJGBench provides a case-type-aware diagnostic evaluation framework comprising 13 metric types and adopts a hybrid paradigm. Experiments reveal no uniform advantage for legal-domain models over general-purpose models, with model strengths varying across diagnostic dimensions. Performance is generally stronger on criminal cases, while civil and administrative cases continue to present substantial challenges.