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LEVA : Using large language models to enhance visual analytics
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Zhao, Yuheng, Zhang, Yixing, Zhang, Yu, Zhao, Xinyi, Wang, Junjie, Shao, Zekai, Turkay, Cagatay and Chen, Siming (2024) LEVA : Using large language models to enhance visual analytics. IEEE Transactions on Visualization and Computer Graphics . pp. 1-17. doi:10.1109/TVCG.2024.3368060 ISSN 1077-2626. (In Press)
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Official URL: http://doi.org/10.1109/TVCG.2024.3368060
Abstract
Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.
Item Type: | Journal Article | ||||||||||||||||||
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Subjects: | P Language and Literature > P Philology. Linguistics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Social Sciences > Centre for Interdisciplinary Methodologies | ||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Visual analytics, Natural language processing (Computer science), Artificial intelligence, Computer graphics, Computational linguistics | ||||||||||||||||||
Journal or Publication Title: | IEEE Transactions on Visualization and Computer Graphics | ||||||||||||||||||
Publisher: | Institute of Electrical and Electronics Engineers | ||||||||||||||||||
ISSN: | 1077-2626 | ||||||||||||||||||
Official Date: | 4 March 2024 | ||||||||||||||||||
Dates: |
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Page Range: | pp. 1-17 | ||||||||||||||||||
DOI: | 10.1109/TVCG.2024.3368060 | ||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||
Publication Status: | In Press | ||||||||||||||||||
Re-use Statement: | © 2024 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||
Date of first compliant deposit: | 6 March 2024 | ||||||||||||||||||
Date of first compliant Open Access: | 6 March 2024 | ||||||||||||||||||
RIOXX Funder/Project Grant: |
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