@inproceedings{jung-etal-2023-enhancing,
title = "Enhancing Task-Oriented Dialog System with Subjective Knowledge: A Large Language Model-based Data Augmentation Framework",
author = "Jung, Haein and
Yeen, Heuiyeen and
Lee, Jeehyun and
Kim, Minju and
Bang, Namo and
Koo, Myoung-Wan",
editor = "Chen, Yun-Nung and
Crook, Paul and
Galley, Michel and
Ghazarian, Sarik and
Gunasekara, Chulaka and
Gupta, Raghav and
Hedayatnia, Behnam and
Kottur, Satwik and
Moon, Seungwhan and
Zhang, Chen",
booktitle = "Proceedings of The Eleventh Dialog System Technology Challenge",
month = sep,
year = "2023",
address = "Prague, Czech Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.dstc-1.18",
pages = "150--165",
abstract = "As Task-Oriented Dialog (TOD) systems have advanced, structured DB systems, which aim to collect relevant knowledge for answering user{'}s questions, have also progressed. Despite these advancements, these methods face challenges when dealing with subjective questions from users. To overcome this, DSTC11 released a subjective-knowledge-based TOD (SK-TOD) dataset and benchmark. This paper introduces a framework that effectively solves SK-TOD tasks by leveraging a Large Language Model (LLM). We demonstrate the proficient use of LLM for each sub-task, including an adapters-based method and knowledge-grounded data augmentation. Our proposed methods, which utilize LLM as an efficient tool, outperform baseline performance and approaches that directly use LLM as a one-step sub-task solver, showing superior task-specific optimization.",
}
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<abstract>As Task-Oriented Dialog (TOD) systems have advanced, structured DB systems, which aim to collect relevant knowledge for answering user’s questions, have also progressed. Despite these advancements, these methods face challenges when dealing with subjective questions from users. To overcome this, DSTC11 released a subjective-knowledge-based TOD (SK-TOD) dataset and benchmark. This paper introduces a framework that effectively solves SK-TOD tasks by leveraging a Large Language Model (LLM). We demonstrate the proficient use of LLM for each sub-task, including an adapters-based method and knowledge-grounded data augmentation. Our proposed methods, which utilize LLM as an efficient tool, outperform baseline performance and approaches that directly use LLM as a one-step sub-task solver, showing superior task-specific optimization.</abstract>
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%0 Conference Proceedings
%T Enhancing Task-Oriented Dialog System with Subjective Knowledge: A Large Language Model-based Data Augmentation Framework
%A Jung, Haein
%A Yeen, Heuiyeen
%A Lee, Jeehyun
%A Kim, Minju
%A Bang, Namo
%A Koo, Myoung-Wan
%Y Chen, Yun-Nung
%Y Crook, Paul
%Y Galley, Michel
%Y Ghazarian, Sarik
%Y Gunasekara, Chulaka
%Y Gupta, Raghav
%Y Hedayatnia, Behnam
%Y Kottur, Satwik
%Y Moon, Seungwhan
%Y Zhang, Chen
%S Proceedings of The Eleventh Dialog System Technology Challenge
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czech Republic
%F jung-etal-2023-enhancing
%X As Task-Oriented Dialog (TOD) systems have advanced, structured DB systems, which aim to collect relevant knowledge for answering user’s questions, have also progressed. Despite these advancements, these methods face challenges when dealing with subjective questions from users. To overcome this, DSTC11 released a subjective-knowledge-based TOD (SK-TOD) dataset and benchmark. This paper introduces a framework that effectively solves SK-TOD tasks by leveraging a Large Language Model (LLM). We demonstrate the proficient use of LLM for each sub-task, including an adapters-based method and knowledge-grounded data augmentation. Our proposed methods, which utilize LLM as an efficient tool, outperform baseline performance and approaches that directly use LLM as a one-step sub-task solver, showing superior task-specific optimization.
%U https://aclanthology.org/2023.dstc-1.18
%P 150-165
Markdown (Informal)
[Enhancing Task-Oriented Dialog System with Subjective Knowledge: A Large Language Model-based Data Augmentation Framework](https://aclanthology.org/2023.dstc-1.18) (Jung et al., DSTC-WS 2023)
ACL