  Large-scale vision-language (V-L) models have demonstrated remarkable
generalization capabilities for downstream tasks through prompt tuning.
However, the mechanisms behind the learned text representations are unknown,
limiting further generalization gains, especially under class imbalance
scenarios. Recent advances in the neural collapse (NC) phenomenon of
vision-only models suggest that the optimal representation structure is the
simplex ETF, which paves the way to study representations in V-L models. In
this paper, we make the first attempt to use NC for examining the
representations in V-L models via prompt tuning. It is found that NC optimality
of text-to-image representations shows a positive correlation with downstream
generalizability, which is more severe under class imbalance settings. To
improve the representations, we propose Neural-collapse-anchored Prompt Tuning
(NPT), a novel method that learns prompts with text and image representations
that satisfy the same simplex ETF. NPT incorporates two regularization terms:
language-modality collapse and multi-modality isomorphism; and it is compatible
with other prompt tuning methods. Extensive experiments show that NPT can
consistently help to improve existing prompt tuning techniques across 11
datasets for both balanced and imbalanced settings.
