Spoken Function Calling: A New Perspective on Spoken Language Understanding for Large Audio Language Models

Abstract:Spoken Language Understanding (SLU) is the core component of task-oriented dialogue systems and a pivotal link in achieving seamless human-agent interaction. While traditional SLU can effectively extract user semantics for closed-set tasks after in-domain supervised fine-tuning, it faces significant challenges in leveraging in-context learning for open-domain tasks due to its ambiguous rule definitions. This work proposes Spoken Function Calling (SFC), a novel semantic understanding perspective that optimizes semantic understanding with structured rule definitions, to evolve beyond traditional closed-set SLU. Specifically, we curate and extend a suite of spoken functions based on traditional SLU datasets, construct a multi-agent system to synthesize the SFC-Bench dataset, evaluate the performance of Large Language Models (LLMs) and Large Audio Language Models (LALMs), and enhance the SFC capabilities of LALMs through post-training. Experiments demonstrate that SFC outperforms traditional SLU, substantially enhancing the semantic extraction accuracy for LLMs and LALMs.

Computation and Language (cs.CL); Multimedia (cs.MM)

arXiv:2608.05126 [cs.CL]

(or arXiv:2608.05126v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.05126 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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