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BiSup: Bidirectional Quantization Error Suppression for Large Language Models
As the size and context length of Large Language Models (LLMs) grow, weight-activation quantization has emerged as a crucial technique for efficient deployment of LLMs.
Attribute Simulation for Item Embedding Enhancement in Multi-interest Recommendation
Although multi-interest recommenders have achieved significant progress in the matching stage, our research reveals that existing models tend to exhibit an under-clustered item embedding space, which leads to a low discernibility between items and hampers item retrieval.
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