从引用到关键性：预测多语言瑞士判例法中的法律判决影响力
From Citations to Criticality: Predicting Legal Decision Influence in the Multilingual Swiss Jurisprudence
AI总结：
针对法院案件积压问题，研究提出Criticality Prediction数据集，通过算法生成双层标签评估案件优先级，并证明在特定领域任务中，拥有大型训练集的微调模型优于大型语言模型。
摘要：
全球许多法院系统不堪重负，导致大量待决案件积压。类似于急诊室的有效分诊系统可以确保对未决案件进行妥善优先排序，从而优化法院系统的时间与资源配置。在本研究中，我们引入了Criticality Prediction数据集，这是一个用于评估案件优先级划分的新型资源。我们的数据集采用双层标注系统：(1) 二元LD-Label，用于识别作为Leading Decisions（LD）发布的案件；(2) 更细粒度的Citation-Label，根据案件的引用频率和时效性对其进行排名，从而实现更细致的评估。与依赖耗费大量资源的人工标注的现有方法不同，我们通过算法生成标签，从而获得了比其他方式大得多的数据集。我们评估了多个多语言模型，包括较小的微调模型和在零样本设置下的大型语言模型。结果表明，得益于我们庞大的训练集，微调模型的表现始终优于其大型对应模型。我们的结果强调，对于像我们这样高度特定领域的任务，大型训练集仍然极具价值。
英文摘要：
Many court systems are overwhelmed all over the world, leading to huge backlogs of pending cases. Effective triage systems, like those in emergency rooms, could ensure proper prioritization of open cases, optimizing time and resource allocation in the court system. In this work, we introduce the Criticality Prediction dataset, a novel resource for evaluating case prioritization. Our dataset features a two-tier labeling system: (1) the binary LD-Label, identifying cases published as Leading Decisions (LD), and (2) the more granular Citation-Label, ranking cases by their citation frequency and recency, allowing for a more nuanced evaluation. Unlike existing approaches that rely on resource-intensive manual annotations, we algorithmically derive labels leading to a much larger dataset than otherwise possible. We evaluate several multilingual models, including both smaller fine-tuned models and large language models in a zero-shot setting. Our results show that the fine-tuned models consistently outperform their larger counterparts, thanks to our large training set. Our results highlight that for highly domain-specific tasks like ours, large training sets are still valuable.
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。
