@inproceedings{zhang-etal-2026-defgen,
title = "{D}ef{G}en-Bench: A Benchmark for {C}hinese Criminal Defence Opinion Generation in {L}egal{AI}",
author = "Zhang, Senbo and
Wang, Qiqi and
Lou, Fanghao and
Chen, Guanyu and
Pan, Yihong and
Li, Huijia and
Liu, Qian",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1635/",
pages = "35378--35392",
ISBN = "979-8-89176-390-6",
abstract = "A defence opinion is an essential step in criminal proceedings, yet it has not been systematically formulated or evaluated as a specific LegalAI task. Grounded in legal principles and practice, we formulate this task as generating a structured defence opinion conditioned jointly on an indictment and the defendant{'}s stated opinion, which often present conflicting claims. We formalize this setting as a dual-perspective generation problem and introduce DefGen-Bench, a benchmark comprising several Chinese criminal cases with expert-reviewed reference defence opinions. We evaluate eight large language models (LLMs) on this task and observe that existing models tend to mirror the defendant{'}s opinion, thereby overlooking more appropriate defence strategies. To address this challenge, we propose Knowledge-Enhanced Highlighted Indictment (KHI), a legal knowledge{--}guided input enhancement method applicable to both open- and closed-source LLMs. Experiments demonstrate consistent improvements across all evaluated LLMs, validating the effectiveness of the proposed approach."
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%0 Conference Proceedings
%T DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI
%A Zhang, Senbo
%A Wang, Qiqi
%A Lou, Fanghao
%A Chen, Guanyu
%A Pan, Yihong
%A Li, Huijia
%A Liu, Qian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-etal-2026-defgen
%X A defence opinion is an essential step in criminal proceedings, yet it has not been systematically formulated or evaluated as a specific LegalAI task. Grounded in legal principles and practice, we formulate this task as generating a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. We formalize this setting as a dual-perspective generation problem and introduce DefGen-Bench, a benchmark comprising several Chinese criminal cases with expert-reviewed reference defence opinions. We evaluate eight large language models (LLMs) on this task and observe that existing models tend to mirror the defendant’s opinion, thereby overlooking more appropriate defence strategies. To address this challenge, we propose Knowledge-Enhanced Highlighted Indictment (KHI), a legal knowledge–guided input enhancement method applicable to both open- and closed-source LLMs. Experiments demonstrate consistent improvements across all evaluated LLMs, validating the effectiveness of the proposed approach.
%U https://aclanthology.org/2026.acl-long.1635/
%P 35378-35392
Markdown (Informal)
[DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI](https://aclanthology.org/2026.acl-long.1635/) (Zhang et al., ACL 2026)
ACL
- Senbo Zhang, Qiqi Wang, Fanghao Lou, Guanyu Chen, Yihong Pan, Huijia Li, and Qian Liu. 2026. DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 35378–35392, San Diego, California, United States. Association for Computational Linguistics.