@inproceedings{hong-etal-2024-exploring,
title = "Exploring the Use of Natural Language Descriptions of Intents for Large Language Models in Zero-shot Intent Classification",
author = "Hong, Taesuk and
Ahn, Youbin and
Lee, Dongkyu and
Shin, Joongbo and
Won, Seungpil and
Han, Janghoon and
Choi, Stanley Jungkyu and
Seo, Jungyun",
editor = "Kawahara, Tatsuya and
Demberg, Vera and
Ultes, Stefan and
Inoue, Koji and
Mehri, Shikib and
Howcroft, David and
Komatani, Kazunori",
booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigdial-1.39",
pages = "458--465",
abstract = "In task-oriented dialogue systems, intent classification is crucial for accurately understanding user queries and providing appropriate services. This study explores the use of intent descriptions with large language models for unseen domain intent classification. By examining the effects of description quality, quantity, and input length management, we identify practical guidelines for optimizing performance. Our experiments using FLAN-T5 3B demonstrate that 1) high-quality descriptions for both training and testing significantly improve accuracy, 2) diversity in training descriptions doesn{'}t greatly affect performance, and 3) off-the-shelf rankers selecting around ten intent options reduce input length without compromising performance. We emphasize that high-quality testing descriptions have a greater impact on accuracy than training descriptions. These findings provide practical guidelines for using intent descriptions with large language models to achieve effective and efficient intent classification in low-resource settings.",
}
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<abstract>In task-oriented dialogue systems, intent classification is crucial for accurately understanding user queries and providing appropriate services. This study explores the use of intent descriptions with large language models for unseen domain intent classification. By examining the effects of description quality, quantity, and input length management, we identify practical guidelines for optimizing performance. Our experiments using FLAN-T5 3B demonstrate that 1) high-quality descriptions for both training and testing significantly improve accuracy, 2) diversity in training descriptions doesn’t greatly affect performance, and 3) off-the-shelf rankers selecting around ten intent options reduce input length without compromising performance. We emphasize that high-quality testing descriptions have a greater impact on accuracy than training descriptions. These findings provide practical guidelines for using intent descriptions with large language models to achieve effective and efficient intent classification in low-resource settings.</abstract>
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%0 Conference Proceedings
%T Exploring the Use of Natural Language Descriptions of Intents for Large Language Models in Zero-shot Intent Classification
%A Hong, Taesuk
%A Ahn, Youbin
%A Lee, Dongkyu
%A Shin, Joongbo
%A Won, Seungpil
%A Han, Janghoon
%A Choi, Stanley Jungkyu
%A Seo, Jungyun
%Y Kawahara, Tatsuya
%Y Demberg, Vera
%Y Ultes, Stefan
%Y Inoue, Koji
%Y Mehri, Shikib
%Y Howcroft, David
%Y Komatani, Kazunori
%S Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F hong-etal-2024-exploring
%X In task-oriented dialogue systems, intent classification is crucial for accurately understanding user queries and providing appropriate services. This study explores the use of intent descriptions with large language models for unseen domain intent classification. By examining the effects of description quality, quantity, and input length management, we identify practical guidelines for optimizing performance. Our experiments using FLAN-T5 3B demonstrate that 1) high-quality descriptions for both training and testing significantly improve accuracy, 2) diversity in training descriptions doesn’t greatly affect performance, and 3) off-the-shelf rankers selecting around ten intent options reduce input length without compromising performance. We emphasize that high-quality testing descriptions have a greater impact on accuracy than training descriptions. These findings provide practical guidelines for using intent descriptions with large language models to achieve effective and efficient intent classification in low-resource settings.
%U https://aclanthology.org/2024.sigdial-1.39
%P 458-465
Markdown (Informal)
[Exploring the Use of Natural Language Descriptions of Intents for Large Language Models in Zero-shot Intent Classification](https://aclanthology.org/2024.sigdial-1.39) (Hong et al., SIGDIAL 2024)
ACL
- Taesuk Hong, Youbin Ahn, Dongkyu Lee, Joongbo Shin, Seungpil Won, Janghoon Han, Stanley Jungkyu Choi, and Jungyun Seo. 2024. Exploring the Use of Natural Language Descriptions of Intents for Large Language Models in Zero-shot Intent Classification. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 458–465, Kyoto, Japan. Association for Computational Linguistics.