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.

The detection of financial misinformation (FMD) is a growing challenge. In this paper, we investigate how task structuring and metadata integration impact the performance of large language models (LLMs) on FMD tasks. We compare two approaches: predicting the label before generating an explanation, and generating the explanation first. Our results reveal that prediction-first models achieve higher F1 scores. We also assess the effect of auxiliary metadata, which surprisingly degraded performance despite its correlation with the labels. Our findings highlight the importance of task order and the need to carefully consider whether to use metadata in limited data settings.