Papers with Legal Judgment Prediction
An Empirical Study on Cross-X Transfer for Legal Judgment Prediction (2022.aacl-main)
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| Challenge: | Cross-lingual transfer learning is understudied in legal NLP but not in legal Judgment Prediction (LJP). |
| Approach: | They explore cross-lingual transfer learning techniques on legal JP using a trilingual Swiss-Judgment-Prediction dataset and adapter-based fine-tuning. |
| Outcome: | The proposed methods improve the model’s performance by augmenting the training dataset with machine-translated versions of the original documents, using a 3 larger training corpus. |
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases (2023.findings-eacl)
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| Challenge: | Legal Judgment Prediction (LJP) is a classification task that uses textual descriptions of case facts as the input. |
| Approach: | They propose to use legal reasoning to map article text to specific case fact text to improve the model's generalization to zero-shot settings. |
| Outcome: | The proposed model outperforms straightforward fact classification and improves zero-shot transfer performance. |
Legal Judgment Prediction via Event Extraction with Constraints (2022.acl-long)
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| 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. |
Annotation Study of Japanese Judgments on Tort for Legal Judgment Prediction with Rationales (2022.lrec-1)
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| Challenge: | An annotation scheme for Japanese judgment documents is proposed to provide a reliable dataset for Legal Judgment Prediction (LJP) the anticipated cost of LJP will be much lower than that of human legal professionals. |
| Approach: | They propose to build an annotation scheme for legal judgment prediction, especially for torts, which extracts decisions and rationales at character-level. |
| Outcome: | The proposed annotation scheme can produce a dataset of Japanese LJP at reasonable reliability. |
Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning (2025.findings-emnlp)
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| Challenge: | Current legal large language models lack trichotomous reasoning capabilities due to the absence of an appropriate benchmark dataset. |
| Approach: | They propose a benchmark dataset for Legal Judgment Prediction with Innocent Verdicts that incorporates trichotomous dogmatics into zero-shot prompting and fine-tuning. |
| Outcome: | The proposed dataset extends three widely-used legal datasets through LLM-based augmentation and manual verification. |
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. |
| Approach: | They propose a method to predict the judgment results for each defendant in multi-defendant cases . they formalize the multi-diffendant judgment process as hierarchical reasoning chains . |
| Outcome: | The proposed method can predict the judgment results for multiple defendants in multi-defendant cases. |
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. |
CMDL: A Large-Scale Chinese Multi-Defendant Legal Judgment Prediction Dataset (2024.findings-acl)
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| Challenge: | Legal Judgment Prediction (LJP) has attracted significant attention in recent years. |
| Approach: | They propose a large-scale Chinese Multi-Defendant LJP dataset . they propose case-level evaluation metrics dedicated for the multi-defendant scenario . |
| Outcome: | The proposed methods show weaknesses when applied to cases involving multiple defendants. |
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. |
An Element-aware Multi-representation Model for Law Article Prediction (2020.emnlp-main)
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| Challenge: | Existing studies have shown that using law articles as external knowledge can improve the performance of the Legal Judgment Prediction. |
| Approach: | They propose a Law Article Element-aware Multi-representation Model which makes full use of law article information and can be used for multi-label samples. |
| Outcome: | The proposed model improves the accuracy of 5.84%, macro F1 of 6.42%, and micro F1 by 4.28% compared with baseline models like TopJudge. |
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. |
| Approach: | They propose a framework that leverages the strength of both LLMs and domain-specific models in the context of precedents. |
| Outcome: | The proposed framework leverages the strength of both LLM and domain models in the context of precedents. |
To Judge or Not to Judge: Can Large Language Models Leverage the Dispute Focus in Legal Judgment? (2026.acl-long)
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| Challenge: | Existing research on large language models for legal judgment prediction fails to address the complexity of civil judicial cases. |
| Approach: | They propose a framework that leverages the dispute focus to guide LLMs through a structured, judge-like cognitive workflow. |
| Outcome: | The proposed framework can guide LLMs through a structured, judge-like cognitive workflow. |