This study delves into harnessing Large Language Models (LLMs) for multi-intent spoken language understanding. By reconfiguring entity slots and introducing Sub-Intent Instructions (SII), the research showcases the potential of LLMs to outperform existing models. The study introduces novel metrics, Entity Slot Accuracy (ESA) and Combined Semantic Accuracy (CSA), to provide a comprehensive evaluation of LLM performance in multi-intent SLU. Through experiments on MixATIS and MixSNIPS datasets, the study highlights the competitive prowess of LLMs and their ability to enhance semantic frame parsing accuracy.
The research addresses limitations such as quantization impact on performance and anticipates enhancements through data selection and prompt refinement. It proposes two new metrics, ESA and CSA, to evaluate LLMs in multi-intent SLU comprehensively. The study aims to catalyze further exploration into the potential of LLMs in advancing multi-intent SLU systems.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Shangjian Yi... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04481.pdfDeeper Inquiries