Constraining to Generalize: Subspace Tuning for Few-shot Generalization of Audio-Language Models

Abstract: Few-shot adaptation of pretrained Audio--LLMs (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 framework with two complementary controls on drift. Structured Subspace Parameterization limits structural deformation, and Residual Anchoring stabilizes adaptation around the zero-shot prior. At inference time, Subspace-aware Gating further suppresses negative transfer for weakly aligned unseen classes. Across 11 audio benchmarks, SubT delivers strong few-shot generalization while remaining efficient, operating directly on precomputed text embeddings without text-encoder backpropagation.