Uksc-Jp: A Legal Judgement Prediction Benchmark For The Uk Supreme Court

Legal judgment prediction (LJP) systems support legal professionals by forecasting case outcomes, aiding in legal preparation and strategy. The development of such systems has been driven largely by benchmark datasets that support the training and evaluation of machine learning (ML) models. However, most existing datasets only contain final judgment texts that include facts written after the court hearing, which are unavailable to practitioners during the pre-hearing phase, limiting the real-world applicability of models trained on them. Additionally, benchmark datasets for Supreme Courts remain scarce, with few notable exceptions. To address both issues, we introduce the first LJP benchmark dataset for the United Kingdom Supreme Court: UKSC-JP, covering 821 cases and supporting two subtasks: (i) legal judgment classification, and (ii) court view generation. Each UKSC case includes a press summary that provides background and does not contain all adjudicated facts, offering a more realistic input for LJP systems. We evaluate several ML models, including large language models (LLMs), across both subtasks. Our results show that, despite recent advances, LJP remains a challenging task, especially when deprived of post-hearing content. We release our code and data resources publicly available at : https://tinyurl.com/2k3met8x