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Recently, advancements in large language models (LLMs) have shown anunprecedented ability across various language tasks. This paper investigatesthe potential application of LLMs to slot filling with noisy ASRtranscriptions, via both in-context learning and task-specific fine-tuning.Dedicated prompt designs and fine-tuning approaches are proposed to improve therobustness of LLMs for slot filling with noisy ASR transcriptions. Moreover, alinearised knowledge injection (LKI) scheme is also proposed to integratedynamic external knowledge into LLMs. Experiments were performed on SLURP toquantify the performance of LLMs, including GPT-3.5-turbo, GPT-4, LLaMA-13B andVicuna-13B (v1.1 and v1.5) with different ASR error rates. The use of theproposed fine-tuning together with the LKI scheme for LLaMA-13B achieved an8.3% absolute SLU-F1 improvement compared to the strong Flan-T5-base baselinesystem on a limited data setup.

Speech-based Slot Filling using Large Language Models