MultiJustice：一个用于多方、多罪名法律预测的中文数据集
MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction
AI总结：
该研究提出中文数据集MultiJustice，用于多被告多罪名法律预测，发现多被告多罪名场景最具挑战性，并公开数据集与代码。
摘要：
法律判决预测提供了一种引人注目的方法，以协助法律从业者和研究人员。然而，一个研究问题仍然相对未被充分探索：在法律判决预测中，多个被告和多个罪名是否应该被分开处理？为了解决这个问题，我们引入了一个新的数据集，即多人多罪名预测（MPMCP），并通过评估几种主流法律大语言模型（LLMs）在四个实际法律判决场景中的表现来寻求答案：（S1）单一被告单一罪名，（S2）单一被告多项罪名，（S3）多名被告单一罪名，以及（S4）多名被告多项罪名。我们在两个法律判决预测任务上评估该数据集，即罪名预测和刑期预测。我们进行了大量实验，发现涉及多名被告和多项罪名的场景（S4）带来了最大的挑战，其次是S2、S3和S1。影响因模型而异，差异显著。例如，在S4与S1相比中，InternLM2的F1分数降低了约4.5%，LogD提高了2.8%，而Lawformer的F1分数降低了约19.7%，LogD提高了19.0%。我们的数据集和代码可在https://github.com/lololo-xiao/MultiJustice-MPMCP获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
