@inproceedings{zhang-etal-2024-ljpcheck,
title = "{LJPC}heck: Functional Tests for Legal Judgment Prediction",
author = "Zhang, Yuan and
Huang, Wanhong and
Feng, Yi and
Li, Chuanyi and
Fei, Zhiwei and
Ge, Jidong and
Luo, Bin and
Ng, Vincent",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.350/",
doi = "10.18653/v1/2024.findings-acl.350",
pages = "5878--5894",
abstract = "Legal Judgment Prediction (LJP) refers to the task of automatically predicting judgment results (e.g., charges, law articles and term of penalty) given the fact description of cases. While SOTA models have achieved high accuracy and F1 scores on public datasets, existing datasets fail to evaluate specific aspects of these models (e.g., legal fairness, which significantly impact their applications in real scenarios). Inspired by functional testing in software engineering, we introduce LJPCHECK, a suite of functional tests for LJP models, to comprehend LJP models' behaviors and offer diagnostic insights. We illustrate the utility of LJPCHECK on five SOTA LJP models. Extensive experiments reveal vulnerabilities in these models, prompting an in-depth discussion into the underlying reasons of their shortcomings."
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<abstract>Legal Judgment Prediction (LJP) refers to the task of automatically predicting judgment results (e.g., charges, law articles and term of penalty) given the fact description of cases. While SOTA models have achieved high accuracy and F1 scores on public datasets, existing datasets fail to evaluate specific aspects of these models (e.g., legal fairness, which significantly impact their applications in real scenarios). Inspired by functional testing in software engineering, we introduce LJPCHECK, a suite of functional tests for LJP models, to comprehend LJP models’ behaviors and offer diagnostic insights. We illustrate the utility of LJPCHECK on five SOTA LJP models. Extensive experiments reveal vulnerabilities in these models, prompting an in-depth discussion into the underlying reasons of their shortcomings.</abstract>
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%0 Conference Proceedings
%T LJPCheck: Functional Tests for Legal Judgment Prediction
%A Zhang, Yuan
%A Huang, Wanhong
%A Feng, Yi
%A Li, Chuanyi
%A Fei, Zhiwei
%A Ge, Jidong
%A Luo, Bin
%A Ng, Vincent
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhang-etal-2024-ljpcheck
%X Legal Judgment Prediction (LJP) refers to the task of automatically predicting judgment results (e.g., charges, law articles and term of penalty) given the fact description of cases. While SOTA models have achieved high accuracy and F1 scores on public datasets, existing datasets fail to evaluate specific aspects of these models (e.g., legal fairness, which significantly impact their applications in real scenarios). Inspired by functional testing in software engineering, we introduce LJPCHECK, a suite of functional tests for LJP models, to comprehend LJP models’ behaviors and offer diagnostic insights. We illustrate the utility of LJPCHECK on five SOTA LJP models. Extensive experiments reveal vulnerabilities in these models, prompting an in-depth discussion into the underlying reasons of their shortcomings.
%R 10.18653/v1/2024.findings-acl.350
%U https://aclanthology.org/2024.findings-acl.350/
%U https://doi.org/10.18653/v1/2024.findings-acl.350
%P 5878-5894
Markdown (Informal)
[LJPCheck: Functional Tests for Legal Judgment Prediction](https://aclanthology.org/2024.findings-acl.350/) (Zhang et al., Findings 2024)
ACL
- Yuan Zhang, Wanhong Huang, Yi Feng, Chuanyi Li, Zhiwei Fei, Jidong Ge, Bin Luo, and Vincent Ng. 2024. LJPCheck: Functional Tests for Legal Judgment Prediction. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5878–5894, Bangkok, Thailand. Association for Computational Linguistics.