Recently, advancements in large language models (LLMs) have shown an unprecedented ability across various language tasks. This paper investigates the potential application of LLMs to slot filling with noisy ASR transcriptions, via both in-context learning and task-specific fine-tuning. Dedicated prompt designs and fine-tuning approaches are proposed to improve the robustness of LLMs for slot filling with noisy ASR transcriptions. Moreover, a linearised knowledge injection (LKI) scheme is also proposed to integrate dynamic external knowledge into LLMs. Experiments were performed on SLURP to quantify the performance of LLMs, including GPT-3.5-turbo, GPT-4, LLaMA-13B and Vicuna-13B (v1.1 and v1.5) with different ASR error rates. The use of the proposed fine-tuning together with the LKI scheme for LLaMA-13B achieved an 8.3% absolute SLU-F1 improvement compared to the strong Flan-T5-base baseline system on a limited data setup.

翻译：近期，大语言模型（LLMs）的进步在各种语言任务中展现出前所未有的能力。本文研究了LLMs在含噪ASR转录文本中通过上下文学习和任务特定微调应用于槽位填充的潜力。我们提出了专门的提示设计和微调方法，以增强LLMs处理含噪ASR转录文本时的鲁棒性。此外，还提出了线性化知识注入（LKI）方案，将动态外部知识整合到LLMs中。在SLURP数据集上进行的实验量化了不同ASR错误率下LLMs（包括GPT-3.5-turbo、GPT-4、LLaMA-13B和Vicuna-13B的v1.1与v1.5版本）的性能。针对LLaMA-13B，采用所提出的微调方法结合LKI方案，在有限数据设置下相比强基线系统Flan-T5-base，实现了8.3%的绝对SLU-F1值提升。