arXiv:2601.18904v2 Announce Type: replace-cross 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 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 training-free, inference-time solution by adapting auditory LLMs through conditioning on a few in-domain demonstrations. In this work, we first show that $\textit{Vanilla ICL}
The rapid advancement of LLMs is pushing the boundaries into auditory domains, revealing challenges in adapting these powerful models to diverse, low-resource speech tasks.
This research addresses a key limitation in current auditory LLMs, offering a path to broader applicability and robustness, especially in scenarios with limited training data, which accelerates the utility of AI in more diverse environments.
The development of 'MetaSICL' offers a more effective, training-free method for adapting auditory LLMs to new tasks, potentially reducing the need for extensive re-training or large new datasets for specific applications.
- · AI developers
- · Speech technology companies
- · Companies with diverse audio data needs
- · Platforms requiring significant proprietary data for auditory AI
- · Traditional fine-tuning methodologies
Auditory LLMs will become more versatile and performant across a wider range of low-resource applications.
The cost and time associated with deploying auditory AI in new language or domain contexts may decrease significantly.
This could accelerate the integration of advanced speech interfaces into everyday devices and specialized industrial applications, including those involving unique auditory signatures.
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Read at arXiv cs.CL