Sina Zarrie


2023

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VIST5: An Adaptive, Retrieval-Augmented Language Model for Visualization-oriented Dialog
Henrik Voigt | Nuno Carvalhais | Monique Meuschke | Markus Reichstein | Sina Zarrie | Kai Lawonn
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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.