Breaking the Manual Annotation Bottleneck: Creating a Comprehensive Legal Case Criticality Dataset through Semi-Automated Labeling

Predicting case criticality helps legal professionals in the court system manage large volumes of case law. This paper introduces the Criticality Prediction dataset, a new resource for evaluating the potential influence of Swiss Federal Supreme Court decisions on future jurisprudence. Unlike existing approaches that rely on resource-intensive manual annotations, we semi-automatically derive labels leading to
a much larger dataset than otherwise possible. Our dataset features a two-tier labeling system: (1) the LD-Label, which identifies cases published as Leading Decisions (LD), and (2) the Citation-Label, which ranks cases by their citation frequency and recency. This allows for a more nuanced evaluation of case importance. We evaluate several multilingual models, including fine-tuned variants and large language models, and find that fine-tuned models consistently outperform zero-shot baselines, demonstrating the need for task-specific adaptation. Our contributions include the introduction of this task and the release of a multilingual
dataset to the research community.