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Chat2VIS: Generating Data Visualizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models
Open accessAbstract
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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“This subdomain aims to enhance the capabilities of AI models to collaborate effectively with humans in complex tasks [274], [35].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [35]
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,252
Loper, E., & Bird, S. (2002) · doi:10.3115/1118108.1118117 · Cited by 3,323
NLTK: The Natural Language ToolkitPreprint
Loper, E., & Bird, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205028 · Cited by 1,893
Gao, T. Y., Dontcheva, M., Adar, E., Liu, Z., & Karahalios, K. G. (2015) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2807442.2807478 · Cited by 244
Narechania, A. A., Srinivasan, A., & Stasko, J. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030378 · Cited by 241
Towards Natural Language Interfaces for Data Visualization: A Survey
Shen, L., Shen, E., Luo, Y., Yang, X., Hu, X., Zhang, X., Tai, Z., & Wang, J. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3148007 · Cited by 234
Eviza: A Natural Language Interface for Visual Analysis
Setlur, V., Battersby, S. E., Tory, M., Gossweiler, R., & Chang, A. X. (2016) · Proceedings of the 29th Annual Symposium on User Interface Software and Technology · doi:10.1145/2984511.2984588 · Cited by 231
Natural Language to Visualization by Neural Machine Translation
Luo, Y., Tang, N., Li, G., Tang, J., Chai, C., & Qin, X. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114848 · Cited by 169
FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System
Yu, B., & Silva, C. T. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934668 · Cited by 147
A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
Wang, Q., Chen, Z., Wang, Y., & Qu, H. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3106142 · Cited by 137
Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks
Luo, Y., Tang, N., Li, G., Chai, C., Li, W., & Qin, X. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457261 · Cited by 110
Towards Natural Language-Based Visualization Authoring
Wang, Y., Hou, Z., Shen, L., Wu, T., Wang, J., Huang, H., Zhang, H., & Zhang, D. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209357 · Cited by 91
Sun, Y., Leigh, J., Johnson, A. E., & Lee, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13544-6_18 · Cited by 86
ADVISor: Automatic Visualization Answer for Natural-Language Question on Tabular Data
Liu, C., Han, Y., Jiang, R., & Yuan, X. (2021) · IEEE Pacific Visualization Symposium · doi:10.1109/pacificvis52677.2021.00010 · Cited by 63
Song, Y., Zhao, X., Wong, R. C.-W., & Jiang, D. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539330 · Cited by 34
Sevi: Speech-to-Visualization through Neural Machine Translation
Tang, J., Luo, Y., Ouzzani, M., Li, G., & Chen, H. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3520150 · Cited by 28
Extracting Knowledge from Web Text with Monte Carlo Tree Search
Liu, G., Li, X., Jiakang, W., Sun, M., & Li, P. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380010 · Cited by 22
DeepEye: Visualizing Your Data by Keyword Search
Qin, X., Luo, Y., Tang, N., & Li, G. (2018) · Movebank · doi:10.5441/002/edbt.2018.42 · Cited by 12
nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization TaskPreprint
Luo, Y., Tang, J., & Li, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.12926 · Cited by 11
Ji, Y., & Gao, S. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 10
Challenges in Designing Natural Language Interfaces for Complex Visual Models
Voigt, H., Meuschke, M., Lawonn, K., & Zarrieß, S. (2021) · PUB – Publications at Bielefeld University (Bielefeld University) · Cited by 4
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Yenduri, G., Ramalingam, M., Selvi, G. C., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., Raj, G. D., Jhaveri, R. H., Prabadevi, B., Wang, W., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3389497 · Cited by 916
Mohamed, A. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-11917-z · Cited by 387
Bengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2024) · IEEE Access · doi:10.1109/access.2024.3397775 · Cited by 470
Privacy and Security Concerns in Generative AI: A Comprehensive Survey
Golda, A., Mekonen, K. A., Pandey, A., Singh, A., Hassija, V., Chamola, V., & Sikdar, B. (2024) · IEEE Access · doi:10.1109/access.2024.3381611 · Cited by 318
Sohail, S. S., Farhat, F., Himeur, Y., Nadeem, M., Madsen, D. Ø., Singh, Y., Atalla, S., & Mansoor, W. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101675 · Cited by 192
“…that the majority of authors have evaluated ChatGPT for its capacity for scientific writing (30 publications), whether in terms of producing data (Maddigan and Susnjak, 2023), writing a scientific article (D’Amico et al., 2023; Anderson et al., 2023; Farhat et al., 2023), or fetching references…”
Metadata
- Authors
- Maddigan, P., & Susnjak, T.
- Affiliations
- Massey University (NZ)
- School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
- Venue
- IEEE Access
- Published
- 2023
- Volume
- 11
- Pages
- 45181-45193
- ISSN
- 2169-3536 (ISSN-L)
- 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
- gold
- doi.org
- 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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