Do Large Language Model Understand Multi-Intent Spoken Language ?

• URL: http://arxiv.org/abs/2403.04481v3
• Date: Mon, 15 Apr 2024 16:24:35 GMT
• Title: Do Large Language Model Understand Multi-Intent Spoken Language ?
• Authors: Shangjian Yin, Peijie Huang, Yuhong Xu, Haojing Huang, Jiatian Chen,
• Abstract summary: 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. We introduce the concept of Sub-Intent Instruction (SII) to amplify the analysis and interpretation of complex, multi-intent communications.
• Score: 5.494472119991781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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