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University of Science and Technology of China
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LLMs for recommendation can now surpass the limitations of static training signals, achieving sustained improvements in ranking accuracy, fairness, and diversity through a dynamically updated Bayesian distillation target.
LLMs can generate recommendations up to 3.1x faster by explicitly modeling token position within items and speculation depth during speculative decoding.
