用大模型和机器学习自动给法律案件排序，提升司法效率
LLMPR: A Novel LLM-Driven Transfer Learning based Petition Ranking Model
- 结合法律文本嵌入与数值特征，用随机森林等模型实现案件优先级排序
- 准确率超99%，排名相关性达0.99，接近人工专家水平
- 适合想提升法院排期效率的研究者或司法科技从业者
印度司法系统长期积压大量未决案件，手动排期效率低且易受主观偏见影响。为解决此问题，我们提出基于大语言模型的案件排序框架LLMPR，利用迁移学习与机器学习对法律文书进行优先级评估。基于包含7,593个标注案件的ILDC数据集，通过DistilBERT、LegalBERT、MiniLM等嵌入技术处理非结构化文本，并融合缺案天数、排名分数、字数等量化指标，训练随机森林、决策树、XGBoost、LightGBM及CatBoost等模型。实验表明，随机森林与决策树表现最优，准确率超过99%，斯皮尔曼等级相关系数达0.99。仅使用数值特征的模型已接近最优（R²=0.988，ρ=0.998），而大模型嵌入带来的增益有限。结果表明，自动化案件排序可有效优化司法流程，缓解积案压力，提升分配公平性。
原文摘要 · Abstract (English)
The persistent accumulation of unresolved legal cases, especially within the Indian judiciary, significantly hampers the timely delivery of justice. Manual methods of prioritizing petitions are often prone to inefficiencies and subjective biases further exacerbating delays. To address this issue, we propose LLMPR (Large Language Model-based Petition Ranking), an automated framework that utilizes transfer learning and machine learning to assign priority rankings to legal petitions based on their contextual urgency. Leveraging the ILDC dataset comprising 7,593 annotated petitions, we process unstructured legal text and extract features through various embedding techniques, including DistilBERT, LegalBERT, and MiniLM. These textual embeddings are combined with quantitative indicators such as gap days, rank scores, and word counts to train multiple machine learning models, including Random Forest, Decision Tree, XGBoost, LightGBM, and CatBoost. Our experiments demonstrate that Random Forest and Decision Tree models yield superior performance, with accuracy exceeding 99% and a Spearman rank correlation of 0.99. Notably, models using only numerical features achieve nearly optimal ranking results (R2 = 0.988, \r{ho} = 0.998), while LLM-based embeddings offer only marginal gains. These findings suggest that automated petition ranking can effectively streamline judicial workflows, reduce case backlog, and improve fairness in legal prioritization.
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