| Challenge: | Existing models fail to locate key event information that determines the judgment results. |
| Approach: | They propose an Event-based Prediction Model with constraints that exploits constraints in LJP. |
| Outcome: | The proposed model surpasses existing models on a standard LJP dataset in English and French. |
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Legal Judgment Prediction: A Reflection on the State of the Art (2026.acl-long)
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| Challenge: | Legal Judgment Prediction (LJP) involves predicting judgment outcomes based on fact descriptions of cases. |
| Approach: | They propose to use argument trees to build automated legal judgment prediction systems that are trustworthy and can be used to predict cases. |
| Outcome: | The proposed model outperforms competitors on standard evaluation datasets and enables pluralistic values to be naturally expressed. |
Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification (2025.naacl-long)
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| Challenge: | Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case. |
| Approach: | They propose a knowledge-enhanced approach that incorporates 'label-level knowledge' to enhance the representation of case facts for each task and 'task-level' knowledge to improve synergy. |
| Outcome: | The proposed method is effective in comparison to state-of-the-art (SOTA) baselines. |
Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction (2025.emnlp-main)
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Junkai Liu, Yujie Tong, Hui Huang, Bowen Zheng, Yiran Hu, Peicheng Wu, Chuan Xiao, Makoto Onizuka, Muyun Yang, Shuyuan Zheng
| Challenge: | Existing studies use legal facts to predict judgments, but legal facts are difficult to obtain in early stages of litigation. |
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LJPCheck: Functional Tests for Legal Judgment Prediction (2024.findings-acl)
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| Challenge: | Existing LJP models fail to evaluate specific aspects of their performance, such as legal fairness and judicial fairness. |
| Approach: | They propose a suite of functional tests for LJP models to comprehend LJp models’ behaviors and offer diagnostic insights. |
| Outcome: | Extensive tests reveal weaknesses in LJP models and provide diagnostic insights. |
Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration (2023.emnlp-main)
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Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang
| Challenge: | Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. |
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| Outcome: | The proposed framework leverages the strength of both LLM and domain models in the context of precedents. |
Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)
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| Challenge: | Existing models lack interpretability due to the neglect of rationale in the prediction process. |
| Approach: | They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability. |
| Outcome: | The proposed framework provides good interactivity and interpretability which enables practical use. |
Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning (2023.findings-emnlp)
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Yougang Lyu, Jitai Hao, Zihan Wang, Kai Zhao, Shen Gao, Pengjie Ren, Zhumin Chen, Fang Wang, Zhaochun Ren
| Challenge: | Existing methods for predicting judgment results for multiple defendants are ineffective. |
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| Outcome: | The proposed method can predict the judgment results for multiple defendants in multi-defendant cases. |
Legal Judgment Prediction via Topological Learning (D18-1)
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| Challenge: | Existing studies focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks. |
| Approach: | They propose a topological multi-task learning framework that incorporates multiple subtasks and DAG dependencies into judgment prediction. |
| Outcome: | The proposed model improves on baselines on all judgment prediction tasks. |
Neural Legal Judgment Prediction in English (P19-1)
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| Challenge: | Recent work on legal judgment prediction has focused on Chinese, but only feature-based models have been considered in English. |
| Approach: | They propose a hierarchical version of BERT which bypasses BERT’s length limitation. |
| Outcome: | The proposed model outperforms existing models in binary violation classification, multi-label classification and case importance prediction. |
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction (2023.findings-emnlp)
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| Challenge: | Existing studies fail to distinguish different classification errors with a standard cross-entropy classification loss and ignore the numbers in the fact description for predicting the term of penalty. |
| Approach: | They propose to extract crime amounts from fact description and use them to learn distinguishable representations to exploit the numbers in the fact description for predicting the term of penalty. |
| Outcome: | The proposed method achieves state-of-the-art results on real-world datasets and ablation studies demonstrate the effectiveness of each component. |