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Cited by 244Data Visualization and AnalyticsTopic ModelingNatural Language Processing Techniques

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Chat2VIS: Generating Data Visualizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models

Open access
Maddigan, Paula, Susnjak, Teo·2023·IEEE Access
DOI 10.1109/access.2023.3274199
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Abstract

The field of data visualisation has long aimed to devise solutions for generating visualisations directly from natural language text. Research in Natural Language Interfaces (NLIs) has contributed towards the development of such techniques. However, the implementation of workable NLIs has always been challenging due to the inherent ambiguity of natural language, as well as in consequence of unclear and poorly written user queries which pose problems for existing language models in discerning user intent. Instead of pursuing the usual path of developing new iterations of language models, this study uniquely proposes leveraging the advancements in pre-trained large language models (LLMs) such as ChatGPT and GPT-3 to convert free-form natural language directly into code for appropriate visualisations. This paper presents a novel system, Chat2VIS, which takes advantage of the capabilities of LLMs and demonstrates how, with effective prompt engineering, the complex problem of language understanding can be solved more efficiently, resulting in simpler and more accurate end-to-end solutions than prior approaches. Chat2VIS shows that LLMs together with the proposed prompts offer a reliable approach to rendering visualisations from natural language queries, even when queries are highly misspecified and underspecified. This solution also presents a significant reduction in costs for the development of NLI systems, while attaining greater visualisation inference abilities compared to traditional NLP approaches that use hand-crafted grammar rules and tailored models. This study also presents how LLM prompts can be constructed in a way that preserves data security and privacy while being generalisable to different datasets. This work compares the performance of GPT-3, Codex and ChatGPT across several case studies and contrasts the performances with prior studies.

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Metadata

Authors
Maddigan, P., & Susnjak, T.
Affiliations
  • Massey University (NZ)
  • School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023
Volume
11
Pages
45181-45193
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2023.3274199
OpenAlex
W4382498938
Cited by
242
References
22
Citations by year
2026: 55 · 2025: 73 · 2024: 92 · 2023: 22
FWCI
21.93
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
natural language interfaces to databases, large language models, prompt engineering, ChatGPT, GPT-3, codices, natural language to code generation, user intent understanding, data privacy

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