@inproceedings{hu-etal-2026-chjgbench,
title = "{C}h{JGB}ench: A Comprehensive Benchmark for {C}hinese Legal Judgment Generation with Diagnostic Evaluation",
author = "Hu, Yinghao and
Kong, Linggang and
Xiao, Qian and
Huang, Yuting and
Zhang, Yunlong and
Zhang, Yujie and
Wu, Yiquan and
Duan, Fei and
Song, Kaisong and
Lin, Jun and
Kuang, Kun and
Wu, Fei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1601/",
pages = "36404--36428",
ISBN = "979-8-89176-459-0",
abstract = "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."
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%0 Conference Proceedings
%T ChJGBench: A Comprehensive Benchmark for Chinese Legal Judgment Generation with Diagnostic Evaluation
%A Hu, Yinghao
%A Kong, Linggang
%A Xiao, Qian
%A Huang, Yuting
%A Zhang, Yunlong
%A Zhang, Yujie
%A Wu, Yiquan
%A Duan, Fei
%A Song, Kaisong
%A Lin, Jun
%A Kuang, Kun
%A Wu, Fei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hu-etal-2026-chjgbench
%X 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.
%U https://aclanthology.org/2026.emnlp-main.1601/
%P 36404-36428
Markdown (Informal)
[ChJGBench: A Comprehensive Benchmark for Chinese Legal Judgment Generation with Diagnostic Evaluation](https://aclanthology.org/2026.emnlp-main.1601/) (Hu et al., EMNLP 2026)
ACL
- Yinghao Hu, Linggang Kong, Qian Xiao, Yuting Huang, Yunlong Zhang, Yujie Zhang, Yiquan Wu, Fei Duan, Kaisong Song, Jun Lin, Kun Kuang, and Fei Wu. 2026. ChJGBench: A Comprehensive Benchmark for Chinese Legal Judgment Generation with Diagnostic Evaluation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 36404–36428, Budapest, Hungary. Association for Computational Linguistics.