Constraining to Generalize: Subspace Tuning for Few-shot Generalization of Audio-Language Models (opens in new tab)

Few-shot adaptation of pretrained Audio--Language Models (ALMs) often improves seen-class performance at the cost of unseen-class generalization, leading to the base-to-new trade-off. We attribute this failure to zero-shot drift in the text embedding space: few-shot tuning can distort inter-class structure and move adapted embeddings far from their pretrained anchors. We therefore propose Subspace Tuning (SubT), a geometry-constrained adaptation...