AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916756254097408 |
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| author | Huang, Yuting Guo, Meitong Wu, Yiquan Li, Ang Liu, Xiaozhong Yin, Keting Sun, Changlong Wu, Fei Kuang, Kun |
| author_facet | Huang, Yuting Guo, Meitong Wu, Yiquan Li, Ang Liu, Xiaozhong Yin, Keting Sun, Changlong Wu, Fei Kuang, Kun |
| contents | Recent advances in LegalAI have primarily focused on individual case judgment analysis, often overlooking the critical appellate process within the judicial system. Appeals serve as a core mechanism for error correction and ensuring fair trials, making them highly significant both in practice and in research. To address this gap, we present the AppealCase dataset, consisting of 10,000 pairs of real-world, matched first-instance and second-instance documents across 91 categories of civil cases. The dataset also includes detailed annotations along five dimensions central to appellate review: judgment reversals, reversal reasons, cited legal provisions, claim-level decisions, and whether there is new information in the second instance. Based on these annotations, we propose five novel LegalAI tasks and conduct a comprehensive evaluation across 20 mainstream models. Experimental results reveal that all current models achieve less than 50% F1 scores on the judgment reversal prediction task, highlighting the complexity and challenge of the appeal scenario. We hope that the AppealCase dataset will spur further research in LegalAI for appellate case analysis and contribute to improving consistency in judicial decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios Huang, Yuting Guo, Meitong Wu, Yiquan Li, Ang Liu, Xiaozhong Yin, Keting Sun, Changlong Wu, Fei Kuang, Kun Computation and Language Recent advances in LegalAI have primarily focused on individual case judgment analysis, often overlooking the critical appellate process within the judicial system. Appeals serve as a core mechanism for error correction and ensuring fair trials, making them highly significant both in practice and in research. To address this gap, we present the AppealCase dataset, consisting of 10,000 pairs of real-world, matched first-instance and second-instance documents across 91 categories of civil cases. The dataset also includes detailed annotations along five dimensions central to appellate review: judgment reversals, reversal reasons, cited legal provisions, claim-level decisions, and whether there is new information in the second instance. Based on these annotations, we propose five novel LegalAI tasks and conduct a comprehensive evaluation across 20 mainstream models. Experimental results reveal that all current models achieve less than 50% F1 scores on the judgment reversal prediction task, highlighting the complexity and challenge of the appeal scenario. We hope that the AppealCase dataset will spur further research in LegalAI for appellate case analysis and contribute to improving consistency in judicial decision-making. |
| title | AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.16514 |