用逻辑引导多阶段推理，提升多被告案件责任判定的可解释性
Logic-Guided Multistage Inference for Explainable Multidefendant Judgment Prediction
- 引入量刑逻辑与定向掩码机制，明确被告角色
- 在自建IMLJP数据集上角色区分准确率显著提升
- 适合法律AI、司法辅助系统研究者参考
犯罪破坏社会安定，法律是维系平衡的关键。在多被告案件中，责任分配复杂且影响公平，需精确区分主犯与从犯角色。然而司法表述常模糊被告职责，阻碍AI分析。为此，本文将量刑逻辑融入预训练Transformer框架，提升多被告案件智能辅助能力并保障法律可解释性。通过定向掩码机制明确角色，并采用对比数据构建策略增强模型对主从犯责任差异的敏感度。预测的有罪标签经广播方式融入回归模型，整合案情描述与法院观点。所提掩码多阶段推理（MMSI）框架在自建的故意伤害案IMLJP数据集上验证，显著提升角色相关责任区分准确率，优于基线模型。本工作为智能司法系统提供可靠解决方案，代码已公开。
原文摘要 · Abstract (English)
Crime disrupts societal stability, making law essential for balance. In multidefendant cases, assigning responsibility is complex and challenges fairness, requiring precise role differentiation. However, judicial phrasing often obscures the roles of the defendants, hindering effective AI-driven analyses. To address this issue, we incorporate sentencing logic into a pretrained Transformer encoder framework to enhance the intelligent assistance in multidefendant cases while ensuring legal interpretability. Within this framework an oriented masking mechanism clarifies roles and a comparative data construction strategy improves the model's sensitivity to culpability distinctions between principals and accomplices. Predicted guilt labels are further incorporated into a regression model through broadcasting, consolidating crime descriptions and court views. Our proposed masked multistage inference (MMSI) framework, evaluated on the custom IMLJP dataset for intentional injury cases, achieves significant accuracy improvements, outperforming baselines in role-based culpability differentiation. This work offers a robust solution for enhancing intelligent judicial systems, with publicly code available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。