Retrieval-Augmented Legal Judgment Prediction with Adaptive Case Retrieval
DOI:
https://doi.org/10.54097/qpp5w246Keywords:
Legal judgment prediction, Retrieval augmentation, Adaptive retrieval, Legal case retrieval, Contrastive learning, Mixture of expertsAbstract
Legal judgment prediction (LJP) models are mostly parametric, although judges routinely consult precedents, and recent retrieval-augmented LJP systems attach a fixed number of precedents to every case. We propose adaptive case retrieval (ACR), which treats a calibrated parametric predictor and the precedent votes of a sparse and a dense retriever at seven retrieval depths as experts, and learns a gate that mixes them from confidence, similarity, label-dispersion and agreement features. The dense retriever is a judgment-consistent contrastive (JCC) encoder whose objective adapts the contrastive knowledge-infusion loss of LegalReasoner by treating cases with agreeing judgments as positives. On CAIL2018-Small with train–test duplicates removed (112 charges, 91 law articles, 11 term intervals), JCC raises 10-nearest-neighbour charge accuracy from 76.9% to 90.7%, and ACR improves a TF-IDF+SVM predictor by 0.62 and 1.28 accuracy points on law-article and prison-term prediction (p < 0.001), matching the best validation-tuned fixed-k interpolation in distribution. Its advantage grows when the precedent memory expands without re-training: with every component fitted on 20% of the labels, expanding the memory to the full training set raises term accuracy by 1.87 points under ACR versus 0.53 points under the fixed scheme, recovering 86.8% of the gap to a fully re-trained model, and the gains do not stem from near-duplicate precedents. A confidence-based routing rule bypasses retrieval for the 70–90% most confident cases while preserving the full charge and article gains, and the complete pipeline costs about 11 ms per case on one CPU core.
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