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Value projection layers are the Achilles' heel of billion-parameter models, revealing surprising sensitivity that could reshape compression strategies.
By disentangling positional information from item embeddings, PASA unlocks more effective sequential modeling in attention mechanisms for recommendation.
Seemingly innocuous data splitting choices in recommender systems can drastically alter model rankings, and SplitLight provides the tools to expose these hidden biases.