@inproceedings{agrawal-etal-2024-dialog,
title = "Dialog Flow Induction for Constrainable {LLM}-Based Chatbots",
author = "Agrawal, Stuti and
Pillai, Pranav and
Uppuluri, Nishi and
Gangi Reddy, Revanth and
Li, Sha and
Tur, Gokhan and
Hakkani-Tur, Dilek and
Ji, Heng",
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-1.6",
pages = "66--77",
abstract = "LLM-driven dialog systems are used in a diverse set of applications, ranging from healthcare to customer service. However, given their generalization capability, it is difficult to ensure that these chatbots stay within the boundaries of the specialized domains, potentially resulting in inaccurate information and irrelevant responses. This paper introduces an unsupervised approach for automatically inducing domain-specific dialog flows that can be used to constrain LLM-based chatbots. We introduce two variants of dialog flow based on the availability of in-domain conversation instances. Through human and automatic evaluation over 24 dialog domains, we demonstrate that our high-quality data-guided dialog flows achieve better domain coverage, thereby overcoming the need for extensive manual crafting of such flows.",
}
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<abstract>LLM-driven dialog systems are used in a diverse set of applications, ranging from healthcare to customer service. However, given their generalization capability, it is difficult to ensure that these chatbots stay within the boundaries of the specialized domains, potentially resulting in inaccurate information and irrelevant responses. This paper introduces an unsupervised approach for automatically inducing domain-specific dialog flows that can be used to constrain LLM-based chatbots. We introduce two variants of dialog flow based on the availability of in-domain conversation instances. Through human and automatic evaluation over 24 dialog domains, we demonstrate that our high-quality data-guided dialog flows achieve better domain coverage, thereby overcoming the need for extensive manual crafting of such flows.</abstract>
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%0 Conference Proceedings
%T Dialog Flow Induction for Constrainable LLM-Based Chatbots
%A Agrawal, Stuti
%A Pillai, Pranav
%A Uppuluri, Nishi
%A Gangi Reddy, Revanth
%A Li, Sha
%A Tur, Gokhan
%A Hakkani-Tur, Dilek
%A Ji, Heng
%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 agrawal-etal-2024-dialog
%X LLM-driven dialog systems are used in a diverse set of applications, ranging from healthcare to customer service. However, given their generalization capability, it is difficult to ensure that these chatbots stay within the boundaries of the specialized domains, potentially resulting in inaccurate information and irrelevant responses. This paper introduces an unsupervised approach for automatically inducing domain-specific dialog flows that can be used to constrain LLM-based chatbots. We introduce two variants of dialog flow based on the availability of in-domain conversation instances. Through human and automatic evaluation over 24 dialog domains, we demonstrate that our high-quality data-guided dialog flows achieve better domain coverage, thereby overcoming the need for extensive manual crafting of such flows.
%U https://aclanthology.org/2024.sigdial-1.6
%P 66-77
Markdown (Informal)
[Dialog Flow Induction for Constrainable LLM-Based Chatbots](https://aclanthology.org/2024.sigdial-1.6) (Agrawal et al., SIGDIAL 2024)
ACL
- Stuti Agrawal, Pranav Pillai, Nishi Uppuluri, Revanth Gangi Reddy, Sha Li, Gokhan Tur, Dilek Hakkani-Tur, and Heng Ji. 2024. Dialog Flow Induction for Constrainable LLM-Based Chatbots. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 66–77, Kyoto, Japan. Association for Computational Linguistics.