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Low-rank prompt learning can match or exceed the performance of dense prompts while slashing trainable parameters, revealing that a fixed token basis suffices for effective adaptation.
MLLMs generate critiques that are more verbose and less selective than human reviewers, raising questions about the validity of traditional evaluation metrics.
