  Out-of-domain (OOD) intent detection aims to examine whether the user's query
falls outside the predefined domain of the system, which is crucial for the
proper functioning of task-oriented dialogue (TOD) systems. Previous methods
address it by fine-tuning discriminative models. Recently, some studies have
been exploring the application of large language models (LLMs) represented by
ChatGPT to various downstream tasks, but it is still unclear for their ability
on OOD detection task.This paper conducts a comprehensive evaluation of LLMs
under various experimental settings, and then outline the strengths and
weaknesses of LLMs. We find that LLMs exhibit strong zero-shot and few-shot
capabilities, but is still at a disadvantage compared to models fine-tuned with
full resource. More deeply, through a series of additional analysis
experiments, we discuss and summarize the challenges faced by LLMs and provide
guidance for future work including injecting domain knowledge, strengthening
knowledge transfer from IND(In-domain) to OOD, and understanding long
instructions.
