SICL-AT: Another way to adapt Auditory LLM to low-resource task

• URL: http://arxiv.org/abs/2601.18904v1
• Date: Mon, 26 Jan 2026 19:15:16 GMT
• Title: SICL-AT: Another way to adapt Auditory LLM to low-resource task
• Authors: Haolong Zheng, Siyin Wang, Zengrui Jin, Mark Hasegawa-Johnson,
• Abstract summary: Auditory Large Language Models (LLMs) have demonstrated strong performance across a wide range of speech and audio understanding tasks.<n>They often struggle when applied to low-resource or unfamiliar tasks.<n>In-Context Learning (ICL) provides a training-free, inference-time solution.
• Score: 34.82834349882226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Auditory Large Language Models (LLMs) have demonstrated strong performance across a wide range of speech and audio understanding tasks. Nevertheless, they often struggle when applied to low-resource or unfamiliar tasks. In case of labeled in-domain data is scarce or mismatched to the true test distribution, direct fine-tuning can be brittle. In-Context Learning (ICL) provides a training-free, inference-time solution by adapting auditory LLMs through conditioning on a few in-domain demonstrations. In this work, we first show that \emph{Vanilla ICL}, improves zero-shot performance across diverse speech and audio tasks for selected models which suggest this ICL adaptation capability can be generalized to multimodal setting. Building on this, we propose \textbf{Speech In-Context Learning Adaptation Training (SICL-AT)}, a post-training recipe utilizes only high resource speech data intending to strengthen model's in-context learning capability. The enhancement can generalize to audio understanding/reasoning task. Experiments indicate our proposed method consistently outperforms direct fine-tuning in low-resource scenario.

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