@inproceedings{guan-etal-2026-generating,
title = "Generating Complete Court Judgments with Large Language Models: A Study on {CMML}",
author = "Guan, Zhongyi and
Hou, Zhitian and
Zeng, Nanli and
Zeng, Kun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1086/",
doi = "10.18653/2026.findings-emnlp.1086",
pages = "22990--23001",
ISBN = "979-8-89176-462-0",
abstract = "Given the criminal fact, a complete legal judgment usually contains two parts: Court View Generation (CVG) for the case and Legal Judgement Prediction (LJP) for each defendant. However, existing studies still cannot support unified modeling and evaluation of complete legal judgments. First, at the data level, existing legal datasets mostly cover either single-defendant scenarios or multi-defendant scenarios, and the attributes predicted by LJP are not rich enough, making it difficult for models to generalize to real-world complete legal judgments. Second, CVG and LJP are usually modeled independently, ignoring their intrinsic connection. Third, the role of auxiliary articles has not been explored. To address these issues, we build CMML (Chinese Mixed-Defendant Multi-Attribute Legal Judgment Dataset), a large-scale dataset that mixes single- and multi-defendant cases with multi-attribute annotations. Based on CMML, we propose a new task, LJP-x, which extends LJP with additional prediction attributes. We further systematically study the interaction between CVG and LJP-x, and explore how auxiliary articles influence both tasks. Our dataset is available at: https://github.com/magicalpig2003/CMML."
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<abstract>Given the criminal fact, a complete legal judgment usually contains two parts: Court View Generation (CVG) for the case and Legal Judgement Prediction (LJP) for each defendant. However, existing studies still cannot support unified modeling and evaluation of complete legal judgments. First, at the data level, existing legal datasets mostly cover either single-defendant scenarios or multi-defendant scenarios, and the attributes predicted by LJP are not rich enough, making it difficult for models to generalize to real-world complete legal judgments. Second, CVG and LJP are usually modeled independently, ignoring their intrinsic connection. Third, the role of auxiliary articles has not been explored. To address these issues, we build CMML (Chinese Mixed-Defendant Multi-Attribute Legal Judgment Dataset), a large-scale dataset that mixes single- and multi-defendant cases with multi-attribute annotations. Based on CMML, we propose a new task, LJP-x, which extends LJP with additional prediction attributes. We further systematically study the interaction between CVG and LJP-x, and explore how auxiliary articles influence both tasks. Our dataset is available at: https://github.com/magicalpig2003/CMML.</abstract>
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%0 Conference Proceedings
%T Generating Complete Court Judgments with Large Language Models: A Study on CMML
%A Guan, Zhongyi
%A Hou, Zhitian
%A Zeng, Nanli
%A Zeng, Kun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F guan-etal-2026-generating
%X Given the criminal fact, a complete legal judgment usually contains two parts: Court View Generation (CVG) for the case and Legal Judgement Prediction (LJP) for each defendant. However, existing studies still cannot support unified modeling and evaluation of complete legal judgments. First, at the data level, existing legal datasets mostly cover either single-defendant scenarios or multi-defendant scenarios, and the attributes predicted by LJP are not rich enough, making it difficult for models to generalize to real-world complete legal judgments. Second, CVG and LJP are usually modeled independently, ignoring their intrinsic connection. Third, the role of auxiliary articles has not been explored. To address these issues, we build CMML (Chinese Mixed-Defendant Multi-Attribute Legal Judgment Dataset), a large-scale dataset that mixes single- and multi-defendant cases with multi-attribute annotations. Based on CMML, we propose a new task, LJP-x, which extends LJP with additional prediction attributes. We further systematically study the interaction between CVG and LJP-x, and explore how auxiliary articles influence both tasks. Our dataset is available at: https://github.com/magicalpig2003/CMML.
%R 10.18653/2026.findings-emnlp.1086
%U https://aclanthology.org/2026.findings-emnlp.1086/
%U https://doi.org/10.18653/2026.findings-emnlp.1086
%P 22990-23001
Markdown (Informal)
[Generating Complete Court Judgments with Large Language Models: A Study on CMML](https://aclanthology.org/2026.findings-emnlp.1086/) (Guan et al., Findings 2026)
ACL