In-Context Adapter Generation for Compositional Generalization in Large Audio Language Models

Overview & Synopsis

Large audio language models (LALMs) have achieved strong performance on standard speech and audio understanding tasks, yet their ability to systematically generalize to novel task compositions remains underexplored. Taking a meta-learning view, we extend Text-to-LoRA (T2L) to LALMs, generating instruction-conditioned LoRA adapters with a Transformer hypernetwork rather than learning a single fixed adapter shared across instructions. To exploit fine-grained task information, we introduce in-context adapter generation (ICAG), a token-level hypernetwork architecture; we call the resulting method T2L-ICAG. We evaluate this approach on two representative LALMs using SIFT-50M for supervised fine-tuning and construct a controlled compositional evaluation suite covering five task classes. Across two LALM backbones, T2L-ICAG consistently improves over static LoRA and also the global embedding-based T2L baseline. Coverage and capacity analyses further show that these gains are larger on structurally out-of-domain tasks and cannot be explained by parameter count alone. These results suggest that generating task-specific adapters from instructions is a promising direction for improving compositional generalization of LALMs.