SpeechLLMs for Large-scale Contextualized Zero-shot Slot Filling

• URL: http://arxiv.org/abs/2510.15851v1
• Date: Fri, 17 Oct 2025 17:45:29 GMT
• Title: SpeechLLMs for Large-scale Contextualized Zero-shot Slot Filling
• Authors: Kadri Hacioglu, Manjunath K E, Andreas Stolcke,
• Abstract summary: Slot filling is a crucial subtask in spoken language understanding (SLU)<n>Recent advent of speech-based large language models (speechLLMs) has opened new avenues for achieving speech understanding tasks.<n>We propose improvements to the training data, architecture, and training strategies to narrow the gap with the upper bound result.
• Score: 10.898666440393896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Slot filling is a crucial subtask in spoken language understanding (SLU), traditionally implemented as a cascade of speech recognition followed by one or more natural language understanding (NLU) components. The recent advent of speech-based large language models (speechLLMs), which integrate speech and textual foundation models, has opened new avenues for achieving speech understanding tasks in a more unified, generative, and instruction-following manner while promising data and compute efficiency with zero-shot abilities, generalizing to unseen slot labels. We address the slot-filling task by creating an empirical upper bound for the task, identifying performance, robustness, and generalization gaps, and proposing improvements to the training data, architecture, and training strategies to narrow the gap with the upper bound result. We show that each of these measures improve performance substantially, while highlighting practical challenges and providing empirical guidance and insights for harnessing these emerging models.

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