Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection

• URL: http://arxiv.org/abs/2402.17256v2
• Date: Mon, 4 Mar 2024 06:04:32 GMT
• Title: Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection
• Authors: Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
• Abstract summary: This paper conducts a comprehensive evaluation of large language models (LLMs) represented by ChatGPT. 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.
• Score: 34.135738700682055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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