Spoken Language Understanding on Unseen Tasks With In-Context Learning

• URL: http://arxiv.org/abs/2505.07731v1
• Date: Mon, 12 May 2025 16:38:43 GMT
• Title: Spoken Language Understanding on Unseen Tasks With In-Context Learning
• Authors: Neeraj Agrawal, Sriram Ganapathy,
• Abstract summary: We introduce a novel approach to robust task-agnostic fine-tuning using randomized class labels.<n>We illustrate that the performance of the speech-text LLMs on an unseen task is significantly improved over standard approaches.
• Score: 32.375855980608286
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Spoken language understanding (SLU) tasks involve diverse skills that probe the information extraction, classification and/or generation capabilities of models. In this setting, task-specific training data may not always be available. While traditional task-specific SLU models are unable to cater to such requirements, the speech-text large language models (LLMs) offer a promising alternative with emergent abilities. However, out of-the-box, our evaluations indicate that the zero/few-shot performance of prominent open-source speech-text LLMs on SLU tasks are not up to the mark. In this paper, we introduce a novel approach to robust task-agnostic fine-tuning using randomized class labels. With this proposed fine-tuning, we illustrate that the performance of the speech-text LLMs on an unseen task is significantly improved over standard approaches. Critically, the proposed approach avoids the requirement of task-specific data annotations for enabling new tasks in speech-text LLMs.

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