





















Abstract:Adaptive prompting mechanisms have been proposed to enhance vision-language models by dynamically tailoring prompts to inputs. However, in frozen few-shot prompt learning with CLIP-style backbones, we systematically observe that adaptive gates and prompt-selection modules often collapse: they produce nearly constant outputs, contribute negligible gradient signals, and frequently fail to outperform fixed prompts. To further explore this issue, we present a systematic diagnostic study to uncover the underlying causes and conditions of adaptation failure. Through controlled experiments across datasets and multiple prompt learning architectures, we identify two recurring failure modes: gradient magnitude imbalance and gate degradation. Our findings invite a re-examination of indiscriminately adding architectural complexity in parameter-efficient learning and clarify when prompt-level adaptive gating is, and is not, effective in this regime.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.09549 [cs.LG] |
| (or arXiv:2605.09549v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.09549 arXiv-issued DOI via DataCite (pending registration) |
From: Yunxuan Fang [view email]
[v1]
Sun, 10 May 2026 14:06:40 UTC (637 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。