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.

该研究探讨了大型语言模型在具有噪声ASR转录的槽位填充中的潜在应用，通过上下文学习和任务特定的微调，提出了专门的提示设计和微调方法来提高大型语言模型在具有噪声ASR转录的槽位填充中的鲁棒性。此外，还提出了一种线性化知识注入方案，将动态外部知识整合到大型语言模型中。在SLURP上进行了实验，评估了各种大型语言模型的性能，包括GPT-3.5-turbo、GPT-4、LLaMA-13B和Vicuna-13B（v1.1和v1.5），以及不同的ASR错误率。在有限数据设置中，使用所提出的微调方法和LKI方案，LLaMA-13B相对于强Flan-T5-base基线系统实现了绝对8.3％的SLU-F1改进。

基于大型语言模型的语音场景填槽