Transformer models for legal judgment prediction show up to 27.2 pp macro-F1 forward degradation across three Ukrainian court epochs, with asymmetric transfer, reduced degradation from legal pretraining, and gains from chronological continual learning.
LEMUR: A corpus for robust fine-tuning of multilingual law embedding models for retrieval
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Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions
Transformer models for legal judgment prediction show up to 27.2 pp macro-F1 forward degradation across three Ukrainian court epochs, with asymmetric transfer, reduced degradation from legal pretraining, and gains from chronological continual learning.