@inproceedings{voigt-etal-2023-vist5,
title = "{VIST}5: An Adaptive, Retrieval-Augmented Language Model for Visualization-oriented Dialog",
author = "Voigt, Henrik and
Carvalhais, Nuno and
Meuschke, Monique and
Reichstein, Markus and
Zarrie, Sina and
Lawonn, Kai",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.5",
doi = "10.18653/v1/2023.emnlp-demo.5",
pages = "70--81",
abstract = "The advent of large language models has brought about new ways of interacting with data intuitively via natural language. In recent years, a variety of visualization systems have explored the use of natural language to create and modify visualizations through visualization-oriented dialog. However, the majority of these systems rely on tailored dialog agents to analyze domain-specific data and operate domain-specific visualization tools and libraries. This is a major challenge when trying to transfer functionalities between dialog interfaces of different visualization applications. To address this issue, we propose VIST5, a visualization-oriented dialog system that focuses on easy adaptability to an application domain as well as easy transferability of language-controllable visualization library functions between applications. Its architecture is based on a retrieval-augmented T5 language model that leverages few-shot learning capabilities to enable a rapid adaptation of the system.",
}
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<abstract>The advent of large language models has brought about new ways of interacting with data intuitively via natural language. In recent years, a variety of visualization systems have explored the use of natural language to create and modify visualizations through visualization-oriented dialog. However, the majority of these systems rely on tailored dialog agents to analyze domain-specific data and operate domain-specific visualization tools and libraries. This is a major challenge when trying to transfer functionalities between dialog interfaces of different visualization applications. To address this issue, we propose VIST5, a visualization-oriented dialog system that focuses on easy adaptability to an application domain as well as easy transferability of language-controllable visualization library functions between applications. Its architecture is based on a retrieval-augmented T5 language model that leverages few-shot learning capabilities to enable a rapid adaptation of the system.</abstract>
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%0 Conference Proceedings
%T VIST5: An Adaptive, Retrieval-Augmented Language Model for Visualization-oriented Dialog
%A Voigt, Henrik
%A Carvalhais, Nuno
%A Meuschke, Monique
%A Reichstein, Markus
%A Zarrie, Sina
%A Lawonn, Kai
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F voigt-etal-2023-vist5
%X The advent of large language models has brought about new ways of interacting with data intuitively via natural language. In recent years, a variety of visualization systems have explored the use of natural language to create and modify visualizations through visualization-oriented dialog. However, the majority of these systems rely on tailored dialog agents to analyze domain-specific data and operate domain-specific visualization tools and libraries. This is a major challenge when trying to transfer functionalities between dialog interfaces of different visualization applications. To address this issue, we propose VIST5, a visualization-oriented dialog system that focuses on easy adaptability to an application domain as well as easy transferability of language-controllable visualization library functions between applications. Its architecture is based on a retrieval-augmented T5 language model that leverages few-shot learning capabilities to enable a rapid adaptation of the system.
%R 10.18653/v1/2023.emnlp-demo.5
%U https://aclanthology.org/2023.emnlp-demo.5
%U https://doi.org/10.18653/v1/2023.emnlp-demo.5
%P 70-81
Markdown (Informal)
[VIST5: An Adaptive, Retrieval-Augmented Language Model for Visualization-oriented Dialog](https://aclanthology.org/2023.emnlp-demo.5) (Voigt et al., EMNLP 2023)
ACL