  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}.
