CrossLJP: An Asymmetric Cross-View Framework for Legal Judgment Prediction

Legal judgment prediction (LJP) uses case facts to predict applicable provisions or judicial outcomes. Prior work has improved LJP through legal pretraining, statutory knowledge, precedents, and label dependencies, yet prediction often relies on a single task-adapted representation. This representation must capture case context and distinguish applicable provisions, but gives the predictor no separate access to pretrained features unchanged by legal supervision. We introduce CrossLJP, an asymmetric cross-view framework for multi-label legal provision prediction that preserves a frozen semantic view alongside an adapted decision view. Cross-attention uses the semantic view to query the decision view, allowing pretrained case features to guide the weighting of adapted features. Across three benchmarks derived from real-world court cases in Chinese, Korean, and English, CrossLJP achieves the highest mean Exact Match (EM), Micro-F1, and Macro-F1 among the evaluated models. These results support preserving pretrained case features and using them to guide prediction from task-adapted features.