Do Large Language Model Understand Multi-Intent Spoken Language ?
<|reference_start|>Do Large Language Model Understand Multi-Intent Spoken Language ?: This research signifies a considerable breakthrough in leveraging Large Language Models (LLMs) for multi-intent spoken language understanding (SLU). Our approach re-imagines the use of entity slots in multi-intent SLU applications, making the most of the generative potential of LLMs within the SLU landscape, leading to the development of the EN-LLM series. Furthermore, we introduce the concept of Sub-Intent Instruction (SII) to amplify the analysis and interpretation of complex, multi-intent communications, which further supports the creation of the ENSI-LLM models series. Our novel datasets, identified as LM-MixATIS and LM-MixSNIPS, are synthesized from existing benchmarks. The study evidences that LLMs may match or even surpass the performance of the current best multi-intent SLU models. We also scrutinize the performance of LLMs across a spectrum of intent configurations and dataset distributions. On top of this, we present two revolutionary metrics - Entity Slot Accuracy (ESA) and Combined Semantic Accuracy (CSA) - to facilitate a detailed assessment of LLM competence in this multifaceted field." Our code and datasets are available at \url{https://github.com/SJY8460/SLM}.<|reference_end|>
@article{yin2024do,
    title={Do Large Language Model Understand Multi-Intent Spoken Language ?},
    author={Shangjian Yin, Peijie Huang, Yuhong Xu, Haojing Huang, Jiatian Chen},
    journal={arXiv preprint arXiv:2403.04481},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04481},
    primaryClass={cs.CL cs.AI}
}