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City University of Hong Kong
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Forget retraining: ReAd dynamically adapts deployed sequential recommendation models to real-time preference shifts by retrieving and integrating collaborative user preference signals at test time.
Forget brute-force distillation: this method uses pedagogical principles to distill LLMs, boosting student model performance on complex reasoning tasks by up to 22.3%.
