A COMPREHENSIVE REVIEW OF GENERATIVE ARTIFICIAL INTELLIGENCE APPLICATIONS IN DATA VISUALIZATION
Using natural language to generate visual representations of data (NL2VIS) is emerging as a promising research direction, driven by the rapid development of Generative AI (GenAI). Given the growing number of studies in this area, this paper conducts a systematic review using the PRISMA method....
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| Format: | Article |
| Language: | English |
| Published: |
Trường Đại học Vinh
2025-06-01
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| Series: | Tạp chí Khoa học |
| Subjects: | |
| Online Access: | https://vujs.vn//api/view.aspx?cid=75b623b9-17ad-4a2e-9f2a-cf97d81ab9a4 |
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| Summary: | Using natural language to generate visual representations of
data (NL2VIS) is emerging as a promising research direction,
driven by the rapid development of Generative AI (GenAI).
Given the growing number of studies in this area, this paper
conducts a systematic review using the PRISMA method.
Based on the analysis of 46 papers published between 2018
and 2024, the findings indicate that: 1) The number of
publications on this topic is increasing, with the IEEE
Transactions on Visualization and Computer Graphics being
the primary outlet; the Generative Adversarial Network
(GAN) model serves as a foundational technology; 2)
Research primarily focuses on integrating large language
models (LLMs), such as ChatGPT, and enhancing the accuracy
and interpretability of AI systems; 3) Current challenges
include the lack of high-quality training data, limited
transparency and interpretability, and ethical concerns related
to the application of GenAI. Future research should aim to
improve generative AI models, design user-centred interfaces,
and address issues such as bias, trust, and privacy in AI-
generated data visualizations. |
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| ISSN: | 1859-2228 |