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27cc3576a6f149e95cf68afc3e25cd6c.zip

Reviewers highlighted that the paper's design choices, specifically "feature sharing," were well-motivated and helped the model stay expressive despite the simplifications. Critical Perspectives

Reviewers from the research community have shared their direct impressions of the work:

The community recognized the extensive evaluations showcasing superior accuracy and query efficiency over 13+ tasks. 27cc3576a6f149e95cf68afc3e25cd6c.zip

Because black-box prompt tuning is a niche field, some reviewers found it difficult to judge exactly how "new" the method was compared to the very latest unpublished research. Community Feedback

This paper introduces a method called designed to improve how we tune large "black-box" models (like CLIP) when we don't have access to their internal code or gradients. Performance and Efficiency Community Feedback This paper introduces a method called

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One reviewer pointed out that the methods ZIP was compared against (like BLACKVIP and BPTVLM) were from 2023, and suggested that more recent 2024 benchmarks should have been included for a fairer comparison. One reviewer pointed out that the methods ZIP

The primary consensus among reviewers is that ZIP significantly reduces the "query cost"—the number of times you have to ask the model for a result—while maintaining or improving accuracy.