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.

Reviewers pointed out that the soft prompt reparameterization design choices were thoroughly tested, including detailed ablation studies.

Evaluators noted superior accuracy across 13+ different tasks and strong performance in "few-shot" settings (learning from very little data).

Reviewers generally agreed that the method offers superior accuracy and efficiency across multiple tasks, supported by thorough ablation studies on design choices.

While the reviews were generally positive, experts noted a few areas for improvement:

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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.