3 papers
cs.CL2026
Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning
StanisÅaw Sójka, Felix Steffek, Matthias Grabmair
Legal outcome prediction must disentangle objective case facts from adjudicative context. Merit-based rulings rely on factual evidence while technical disposals may hinge on judici…
cs.CL2026
Generating Legal Commentaries from Case Databases via Retrieval, Clustering, and Generation
Max Prior, Niklas Wais, Matthias Grabmair
We present a fully automated pipeline that transforms large collections of court decisions into legal commentaries for statutes - without providing any handcrafted doctrinal framew…
cs.CL2026
Exploiting LLM-as-a-Judge Disposition on Free Text Legal QA via Prompt Optimization
Mohamed Hesham Elganayni, Runsheng Chen, Sebastian Nagl +1
This work explores the role of prompt design and judge selection in LLM-as-a-Judge evaluations of free text legal question answering. We examine whether automatic task prompt optim…