@inproceedings{wang-etal-2024-beyond-known,
title = "Beyond the Known: Investigating {LLM}s Performance on Out-of-Domain Intent Detection",
author = "Wang, Pei and
He, Keqing and
Wang, Yejie and
Song, Xiaoshuai and
Mou, Yutao and
Wang, Jingang and
Xian, Yunsen and
Cai, Xunliang and
Xu, Weiran",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.210",
pages = "2354--2364",
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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%0 Conference Proceedings
%T Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection
%A Wang, Pei
%A He, Keqing
%A Wang, Yejie
%A Song, Xiaoshuai
%A Mou, Yutao
%A Wang, Jingang
%A Xian, Yunsen
%A Cai, Xunliang
%A Xu, Weiran
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F wang-etal-2024-beyond-known
%X 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.
%U https://aclanthology.org/2024.lrec-main.210
%P 2354-2364
Markdown (Informal)
[Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection](https://aclanthology.org/2024.lrec-main.210) (Wang et al., LREC-COLING 2024)
ACL
- Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, and Weiran Xu. 2024. Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2354–2364, Torino, Italia. ELRA and ICCL.