GLIER reformulates legal case retrieval as generative inference over latent legal variables like charges and elements, then fuses generative, structural, and lexical signals, outperforming baselines on LeCaRD datasets with strong performance at 10% training data.
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SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.
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GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval
GLIER reformulates legal case retrieval as generative inference over latent legal variables like charges and elements, then fuses generative, structural, and lexical signals, outperforming baselines on LeCaRD datasets with strong performance at 10% training data.
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Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer
SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.