MetaSICL: Adapting Audiroty LLM via Meta Speech In-Context Learning

ArXi:2601.18904v2 Announce Type: replace-cross Auditory Large Language Models (LLMs) have nstrated strong performance across a wide range of speech and audio understanding tasks. Nevertheless, they often struggle when applied to low-resource tasks. In case in-domain labeled data are scarce or mismatched with the true test distribution, direct fine-tuning can be brittle. In-Context Learning (ICL) provides a