MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915379599638528 |
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| author | Wang, Xiao Pei, Jiahuan Shui, Diancheng Han, Zhiguang Sun, Xin Zhu, Dawei Shen, Xiaoyu |
| author_facet | Wang, Xiao Pei, Jiahuan Shui, Diancheng Han, Zhiguang Sun, Xin Zhu, Dawei Shen, Xiaoyu |
| contents | Legal judgment prediction offers a compelling method to aid legal practitioners and researchers. However, the research question remains relatively under-explored: Should multiple defendants and charges be treated separately in LJP? To address this, we introduce a new dataset namely multi-person multi-charge prediction (MPMCP), and seek the answer by evaluating the performance of several prevailing legal large language models (LLMs) on four practical legal judgment scenarios: (S1) single defendant with a single charge, (S2) single defendant with multiple charges, (S3) multiple defendants with a single charge, and (S4) multiple defendants with multiple charges. We evaluate the dataset across two LJP tasks, i.e., charge prediction and penalty term prediction. We have conducted extensive experiments and found that the scenario involving multiple defendants and multiple charges (S4) poses the greatest challenges, followed by S2, S3, and S1. The impact varies significantly depending on the model. For example, in S4 compared to S1, InternLM2 achieves approximately 4.5% lower F1-score and 2.8% higher LogD, while Lawformer demonstrates around 19.7% lower F1-score and 19.0% higher LogD. Our dataset and code are available at https://github.com/lololo-xiao/MultiJustice-MPMCP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06909 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction Wang, Xiao Pei, Jiahuan Shui, Diancheng Han, Zhiguang Sun, Xin Zhu, Dawei Shen, Xiaoyu Computation and Language Artificial Intelligence Legal judgment prediction offers a compelling method to aid legal practitioners and researchers. However, the research question remains relatively under-explored: Should multiple defendants and charges be treated separately in LJP? To address this, we introduce a new dataset namely multi-person multi-charge prediction (MPMCP), and seek the answer by evaluating the performance of several prevailing legal large language models (LLMs) on four practical legal judgment scenarios: (S1) single defendant with a single charge, (S2) single defendant with multiple charges, (S3) multiple defendants with a single charge, and (S4) multiple defendants with multiple charges. We evaluate the dataset across two LJP tasks, i.e., charge prediction and penalty term prediction. We have conducted extensive experiments and found that the scenario involving multiple defendants and multiple charges (S4) poses the greatest challenges, followed by S2, S3, and S1. The impact varies significantly depending on the model. For example, in S4 compared to S1, InternLM2 achieves approximately 4.5% lower F1-score and 2.8% higher LogD, while Lawformer demonstrates around 19.7% lower F1-score and 19.0% higher LogD. Our dataset and code are available at https://github.com/lololo-xiao/MultiJustice-MPMCP. |
| title | MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.06909 |