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Found 5 papers, 1 papers with code
Learn When (not) to Trust Language Models: A Privacy-Centric Adaptive Model-Aware Approach
Despite their great success, the knowledge provided by the retrieval process is not always useful for improving the model prediction, since in some samples LLMs may already be quite knowledgeable and thus be able to answer the question correctly without retrieval.
Foundation Models for Recommender Systems: A Survey and New Perspectives
Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs).
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set.
Improved Knowledge Distillation via Adversarial Collaboration
To achieve this goal, a small student model is trained to exploit the knowledge of a large well-trained teacher model.
Semi-Online Knowledge Distillation
However, to the best of our knowledge, KD and DML have never been jointly explored in a unified framework to solve the knowledge distillation problem.
