To assist in reviewing the standardization of sentencing in cases and determining whether a sentence is disproportionately light, reasonable, or disproportionately heavy, two datasets for predicting sentencing standardization are constructed, and a multi-source knowledge aggregation-based model for sentencing standardization prediction is proposed. The model employs a large language model to represent criminal cases as knowledge graphs, from which similar cases are generated. To integrate knowledge from criminal law and similar cases, a multi-source attention module is designed in the model. The multi-source features generated by this module, together with case representations, are used for sentencing standardization prediction. Comparative experimental results on sentencing standardization prediction show that the F1-score of the proposed model is higher than that of other comparative models. Ablation experiments and case analyses demonstrate that criminal law and similar cases play an important auxiliary role in sentencing standardization prediction. 

为辅助审查案件量刑是否规范, 判断量刑属于畸轻、合理或畸重, 构建两个量刑规范性预测数据集, 同时提出一种基于多源知识聚合的量刑规范性预测模型。该模型利用大语言模型, 将刑事案件表示为知识图谱, 通过知识图谱生成案件的类案。为聚合刑法和类案知识, 在模型中设计多源注意力模块, 将该模块生成的多源特征与案件表示一起用于量刑规范性预测。量刑规范性预测对比实验结果表明, 所提模型的F1值高于其他对比模型。消融实验和案例分析表明, 刑法和类案对量刑规范性预测有重要辅助作用。