Chat2VIS: Generating Data Visualizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models
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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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Interactive and Cooperative AI aims to enhance capabilities of AI models to collaborate effectively with humans in complex tasks.
“This subdomain aims to enhance the capabilities of AI models to collaborate effectively with humans in complex tasks [274], [35].”
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Cites
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 · see citing works
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 · see citing works
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 48,293 · see citing works
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 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
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 7,171 · see citing works
Loper, E., & Bird, S. (2002) · doi:10.3115/1118108.1118117 · Cited by 3,248 · see citing works
NLTK: The Natural Language ToolkitPreprint
Loper, E., & Bird, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205028 · Cited by 1,893 · see citing works
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 · see citing works
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 · see citing works
Narechania, A. A., Srinivasan, A., & Stasko, J. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030378 · Cited by 209 · see citing works
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 195 · see citing works
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 157 · see citing works
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 · see citing works
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 · see citing works
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 90 · see citing works
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 · see citing works
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 73 · see citing works
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 58 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Y. Song, X. Zhao, R. C.-W. Wong, and D. Jiang, ''RGVisNet: A hybrid retrieval-generation neural framework towards automatic data visualiza- tion generation,'' in Proc. 28th ACM SIGKDD Conf. Knowl. Discovery Data Mining, Aug. 2022, pp. 1646-1655.
- T. Gao, M. Dontcheva, E. Adar, Z. Liu, and K. G. Karahalios, ''DataTone: Managing ambiguity in natural language interfaces for data visualization,'' in Proc. 28th Annu. ACM Symp. User Interface Softw. Technol., Nov. 2015, pp. 489-500.
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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 759 · see citing works
Bengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2023) · IEEE Access · doi:10.1109/access.2024.3397775 · Cited by 286 · see citing works
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 269 · see citing works
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 · see citing works
“…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…”
Williamson, S. M., & Prybutok, V. (2024) · Information · doi:10.3390/info15060299 · Cited by 146 · see citing works
From concerns to benefits: A comprehensive study of ChatGPT usage in education
Jo, H. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00471-4 · Cited by 146 · see citing works
Towards an understanding of large language models in software engineering tasks
Zheng, Z., Ning, K., Zhong, Q., Chen, J., Chen, W., Guo, L., Wang, W., & Wang, Y. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10602-0 · Cited by 149 · see citing works
Lian, Y., Tang, H.-T., Xiang, M., & Dong, X.-F. (2023) · Technology and Society · doi:10.1016/j.techsoc.2023.102442 · Cited by 113 · see citing works
ChartGPT: Leveraging LLMs to Generate Charts From Abstract Natural Language
Tian, Y., Cui, W., Deng, D., Yi, X., Yang, Y., Zhang, H., & Wu, Y. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3368621 · Cited by 122 · see citing works
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“…success, including rapid adoption by over 100 million users shortly after launch, which underscores a robust market demand for natural language AI and has catalyzed interdisciplinary research into its applications in sectors like education, healthcare, and commerce [8], [50], [51], [52], [53].”
BackgroundGenerative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation
Sufi, F. K. (2024) · Information · doi:10.3390/info15020099 · Cited by 97 · see citing works
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Mohaidat, T., & Khalil, K. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3377147 · Cited by 97 · see citing works
Large language models for life cycle assessments: Opportunities, challenges, and risks
Preuss, N., Alshehri, A. S., & You, F. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.142824 · Cited by 71 · see citing works
GPT revolution and digital entrepreneurial intentions
Abaddi, S. (2023) · Journal of Entrepreneurship in Emerging Economies · doi:10.1108/jeee-07-2023-0260 · Cited by 81 · see citing works
Gemini-the most powerful LLM: Myth or Truth
Islam, R., & Ahmed, I. (2024) · Information Communication Technologies Conference (ICTC) · doi:10.1109/ictc61510.2024.10602253 · Cited by 84 · see citing works
LEVA: Using Large Language Models to Enhance Visual Analytics
Zhao, Y., Zhang, Y., Zhang, Y., Zhao, X., Wang, J., Shao, Z., Turkay, Ç., & Chen, S. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3368060 · Cited by 65 · see citing works
Schetinger, V., Bartolomeo, S. D., El‐Assady, M., McNutt, A. M., Miller, M., Passos, J. P. A., & Adams, J. L. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14841 · Cited by 54 · see citing works
Hassija, V., Chakrabarti, A., Singh, A., Chamola, V., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3339553 · Cited by 53 · see citing works
An Overview of Digital Twin Technology for Power Electronics: State-of-the-Art and Future Trends
Wu, C., Cui, Z., Xia, Q., Yue, J., & Lyu, F. (2025) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2025.3570638 · Cited by 55 · see citing works
VisEval: A Benchmark for Data Visualization in the Era of Large Language Models
Chen, N., Zhang, Y., Xu, J., Ren, K., & Yang, Y. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456320 · Cited by 53 · see citing works
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
Zhang, W., Wang, Y., Song, Y., Wei, V. J., Tian, Y., Qi, Y., Chan, J. H., Wong, R. C.-W., & Yang, H. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3400824 · Cited by 53 · see citing works
Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
Kavaz, E., Puig, A., & Rodríguez, I. (2023) · Applied Sciences · doi:10.3390/app13127025 · Cited by 42 · see citing works
Enhancing Data Literacy On-Demand: LLMs as Guides for Novices in Chart Interpretation
Choe, K., Lee, C., Lee, S., Song, J., Cho, A., Kim, N. W., & Seo, J. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3413195 · Cited by 41 · see citing works
An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks
Bendeck, A., & Stasko, J. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456155 · Cited by 40 · see citing works
Under the Influence: A Survey of Large Language Models in Fake News Detection
Kuntur, S., Wróblewska, A., Paprzycki, M., & Ganzha, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3471735 · Cited by 43 · see citing works
PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models
Fang, C. M., Danry, V., Whitmore, N. W., Bao, A., Hutchison, A., Pierce, C., & Maes, P. (2024) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi62660.2024.10913781 · Cited by 57 · see citing works
Katib, I., Assiri, F. Y., Abdushkour, H. A., Hamed, D., & Ragab, M. (2023) · Mathematics · doi:10.3390/math11153400 · Cited by 38 · see citing works
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Gupta, B. B., Gaurav, A., & Arya, V. (2024) · Enterprise Information Systems · doi:10.1080/17517575.2024.2310846 · Cited by 37 · see citing works
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Vázquez, P. (2024) · IEEE Pacific Visualization Conference (PacificVis) · doi:10.1109/pacificvis60374.2024.00049 · Cited by 37 · see citing works
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Kumar, V., Gleyzer, L., Kahana, A., Shukla, K., & Karniadakis, G. E. (2023) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2023049518 · Cited by 34 · see citing works
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Madani, S., Tavasoli, A., Astaneh, Z. K., & Pineau, P. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.06.051 · Cited by 31 · see citing works
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Sufi, F. K. (2024) · Information · doi:10.3390/info15050264 · Cited by 29 · see citing works
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Li, Y., Shi, J., & Zhang, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3385682 · Cited by 27 · see citing works
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Wahidur, R. S. M., Tashdeed, I., Kaur, M., & Lee, H.-N. (2024) · IEEE Access · doi:10.1109/access.2024.3350638 · Cited by 28 · see citing works
Wang, J., Ye, Q., Liu, L., Guo, N. L., & Hu, G. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00576-z · Cited by 25 · see citing works
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He, Y., Xu, K., Cao, S., Shi, Y., Chen, Q., & Cao, N. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3542504 · Cited by 37 · see citing works
Beyond Generating Code: Evaluating GPT on a Data Visualization Course
Chen, Z., Zhang, C., Wang, Q., Troidl, J., Warchol, S., Beyer, J., Gehlenborg, N., & Pfister, H. (2023) · IEEE VIS Workshop on Visualization Education, Literacy, and Activities (EduVis) · doi:10.1109/eduvis60792.2023.00009 · Cited by 23 · see citing works
Çelik, S. U. (2025) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2025.116657 · Cited by 28 · see citing works
Zahid, I. A., Joudar, S. S., Albahri, A., Albahri, O., Alamoodi, A., Santamaría, J., & Alzubaidi, L. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200431 · Cited by 25 · see citing works
Stan, M. M., Dumitru, C., & Bucuroiu, F. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13396-w · Cited by 32 · see citing works
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Wang, H. W., Gordon, M., Battle, L., & Heer, J. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456350 · Cited by 20 · see citing works
Wang, C., Lee, B., Drucker, S. M., Marshall, D., & Gao, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713296 · Cited by 30 · see citing works
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Zaazaa, O., & Bakkali, H. E. (2024) · Journal of Metaverse · doi:10.57019/jmv.1489060 · Cited by 20 · see citing works
prompt4vis: prompting large language models with example mining for tabular data visualization
Li, S., Chen, X., Song, Y., Song, Y., Zhang, C., Hao, F., & Chen, L. (2025) · The VLDB Journal · doi:10.1007/s00778-025-00912-0 · Cited by 17 · see citing works
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Routray, S. K., Javali, A., Sharmila, K. P., Jha, M. K., Pappa, M. B., & Singh, M. (2023) · International Conference on Computer Science and Emerging Technologies (CSET) · doi:10.1109/cset58993.2023.10346621 · Cited by 17 · see citing works
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Li, J., Zhang, M., Li, N., Weyns, D., Jin, Z. G., & Tei, K. (2024) · International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) · doi:10.1145/3643915.3644088 · Cited by 17 · see citing works
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Sänger, M., Mecquenem, N. D., Lewińska, K. E., Bountris, V., Lehmann, F., Leser, U., & Kosch, T. (2024) · GigaScience · doi:10.1093/gigascience/giae030 · Cited by 15 · see citing works
ChatGrid: Power Grid Visualization Empowered by a Large Language Model
Jin, S., & Abhyankar, S. G. (2024) · doi:10.1109/energyvis63885.2024.00007 · Cited by 15 · see citing works
From Detection to Application: Recent Advances in Understanding Scientific Tables and Figures
Huang, J., Chen, H., Yu, F., & Lu, W. P. (2024) · ACM Computing Surveys · doi:10.1145/3657285 · Cited by 14 · see citing works
ContextMate: a context-aware smart agent for efficient data analysis
Jadoon, A. K., Yu, C., & Shi, Y. (2024) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-023-00144-7 · Cited by 14 · see citing works
Khan, Z. A., Xia, Y., Ali, S., Khan, J. A., Askar, S., Abouhawwash, M., & El-Rashidy, N. M. (2023) · IEEE Access · doi:10.1109/access.2023.3312764 · Cited by 14 · see citing works
Pair programming with ChatGPT for sampling and estimation of copulas
Górecki, J. (2023) · Computational Statistics · doi:10.1007/s00180-023-01437-2 · Cited by 14 · see citing works
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Hutchinson, M., Jianu, R., Slingsby, A., & Madhyastha, P. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104246 · Cited by 13 · see citing works
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Ros, F. D., Soprano, M., Gaspero, L. D., & Roitero, K. (2026) · ACM Computing Surveys · doi:10.1145/3801961 · Cited by 24 · see citing works
Human-AI in affordance perspective: a study on ChatGPT users in the context of Indonesian users
Haqqu, R., Zahrani, A. R., Wulandari, A., Ersyad, F. A., & Adim, A. K. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1623029 · Cited by 12 · see citing works
Ram, G. K. S., & Muthumanikandan, V. (2024) · IEEE Access · doi:10.1109/access.2024.3465541 · Cited by 11 · see citing works
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Al-Safi, H., IBRAHIM, H., & Steenson, D. P. (2025) · Sensors · doi:10.3390/s25123809 · Cited by 11 · see citing works
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Yang, J., Wang, Y., Lou, Y., Wen, M., & Zhang, L. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3616331 · Cited by 16 · see citing works
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Duarte, D., Costa, R., Bizarro, P., & Duarte, C. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15099 · Cited by 10 · see citing works
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Ghosh, A., & Deepa, K. (2024) · IEEE Access · doi:10.1109/access.2024.3468996 · Cited by 10 · see citing works
Xu, Z., & Wall, E. (2024) · Visualization · doi:10.1109/vis55277.2024.00033 · Cited by 12 · see citing works
Lu, B., Francescutto, L., Howie, S., Lin, H. Z., Wu, Q., Hedley, N., Jamali, A., & McDonald, I. (2025) · Big Earth Data · doi:10.1080/20964471.2025.2480446 · Cited by 10 · see citing works
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Weng, L., Tang, Y., Feng, Y., Chang, Z., Chen, R., Feng, H., Hou, C., Huang, D., Li, Y., Rao, H., et al. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00326 · Cited by 15 · see citing works
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Egersdoerfer, C., Sareen, A., Bez, J. L., Byna, S., & Dai, D. (2024) · ACM Workshop on Hot Topics in Storage and File Systems (HotStorage) · doi:10.1145/3655038.3665950 · Cited by 9 · see citing works
Palanisamy, B., Chakrabarti, A., Singh, A., Hassija, V., Chalapathi, G., & Singh, A. R. (2024) · IEEE Access · doi:10.1109/access.2024.3521596 · Cited by 10 · see citing works
Multiagent Actor-Critic Generative AI for Query Resolution and Analysis
Ur Rahman, M. W., Nevarez, R., Mim, L. T., & Hariri, S. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3544173 · Cited by 8 · see citing works
Benchmarking Visual Language Models on Standardized Visualization Literacy Tests
Pandey, S., & Ottley, A. (2025) · Computer Graphics Forum · doi:10.1111/cgf.70137 · Cited by 8 · see citing works
Jiao, Y., Han, J., & Huang, C. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00982-0 · Cited by 8 · see citing works
Demonstrating ChatGPT possibilities for the libraries
Zеmskov, A., & Telitsyna, A. Y. (2024) · Scientific and Technical Libraries · doi:10.33186/1027-3689-2024-4-131-145 · Cited by 8 · see citing works
Musazade, N., Mezei, J., & Wang, X. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-60227-6_5 · Cited by 7 · see citing works
Large Language Models for the Automated Analysis of Optimization Algorithms
Sartori, C. C., Blum, C., & Ochoa, G. (2024) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3638529.3654086 · Cited by 7 · see citing works
Context-Aware Summarization for PDF Documents using Large Language Models
Ramprasad, A., & Sivakumar, P. (2024) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca62351.2024.00044 · Cited by 7 · see citing works
A Scoping Review on the Educational Applications of Generative AI in Primary and Secondary Education
Ahn, S., Lee, J., Park, J., Jung, S., & Song, J. (2024) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2024.27.6.002 · Cited by 8 · see citing works
AI Translation Tools in Language Learning
Ayyaz, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9077-1.ch003 · Cited by 7 · see citing works
Galdino, M., Hamann, T., Abdelrazeq, A., & Isenhardt, I. (2025) · Engineering Reports · doi:10.1002/eng2.70437 · Cited by 8 · see citing works
Baek, D., Kim, J., & Lee, H.-C. (2024) · IEEE Access · doi:10.1109/access.2024.3447737 · Cited by 9 · see citing works
Jiang, C., Lei, W., Kuang, E., Han, T., & Fan, M. (2023) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3597638.3608423 · Cited by 7 · see citing works
Data Sorting Influence on Short Text Manual Labeling Quality for Hierarchical Classification
Narushynska, O., Teslyuk, V., Doroshenko, A., & Arzubov, M. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8040041 · Cited by 11 · see citing works
Wu, H., Wei, W., Li, S.-H., & Chen, M. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3538785 · Cited by 6 · see citing works
The Role of Accuracy and Validation Effectiveness in Conversational Business Analytics
Alparslan, A. (2025) · IEEE Access · doi:10.1109/access.2025.3540975 · Cited by 6 · see citing works
Evaluating LLMs for visualization generation and understanding
Khan, S. R., Chandak, V., & Mukherjea, S. (2025) · Discover Data · doi:10.1007/s44248-025-00036-4 · Cited by 6 · see citing works
VOICE: Visual Oracle for Interaction, Conversation, and Explanation
Jia, D., Irger, A., Besançon, L., Strnad, O., Luo, D., Björklund, J., Kouyoumdjian, A., Ynnerman, A., & Viola, I. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3579956 · Cited by 6 · see citing works
Nouri, A., & Hu, M. (2025) · iScience · doi:10.1016/j.isci.2025.114589 · Cited by 7 · see citing works
Analyzing the impact of LLMs in Data Science lifecycle: a systematic mapping study
Chintakunta, S. S., Nascimento, N., & Guimarães, E. T. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01041-9 · Cited by 6 · see citing works
Maio, C. D., Fenza, G., Furno, D., Grauso, T., & Loia, V. (2024) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom62040.2024.10721684 · Cited by 6 · see citing works
Accelerating Industry 4.0 and 5.0: The Potential of Generative Artificial Intelligence
Boareto, P. A., Szejka, A. L., Loures, E. D. F. R., Deschamps, F., & Santos, E. A. P. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-80760-2_29 · Cited by 6 · see citing works
Kim, S., Yu, Y., & Seo, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-97498-y · Cited by 6 · see citing works
Yang, C., Liu, Y., & Yin, C. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100637 · Cited by 5 · see citing works
Zhang, C., Dong, Y. D., Wang, Y., Han, Y., Shan, G., & Tang, B. (2025) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2025.3581560 · Cited by 5 · see citing works
Hong, M.-H., & Crisan, A. (2025) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3744750 · Cited by 6 · see citing works
Joung, E., & Kim, Y. R. (2025) · School Science and Mathematics · doi:10.1111/ssm.18393 · Cited by 5 · see citing works
DancingBoard: Streamlining the Creation of Motion Comics to Enhance Narratives
Chen, L., Li, S., Li, Z., & Li, Q. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712167 · Cited by 6 · see citing works
Yang, H., Yang, Z., Zhao, R., Li, X., & Rao, G. (2024) · International Conference on Asian Language Processing · doi:10.1109/ialp63756.2024.10661162 · Cited by 5 · see citing works
The Role of ChatGPT and Artificial Intelligence in Education
Martínez, M. Á. Q., Tumaille-Quintana, D.-S., Moran-Burgos, A.-D., & Ríos, M. D. G. (2024) · IEEE Colombian Conference on Communications and Computing (COLCOM) · doi:10.1109/colcom62950.2024.10720308 · Cited by 5 · see citing works
Framing The Future: Exploring AI Narratives in Indonesian Online Media Using Topic Modelling
(2024) · Jurnal Komunikasi Indonesia · doi:10.7454/jkmi.v13i2.1245 · Cited by 5 · see citing works
Exploring the roles of AI-Assisted ChatGPT in the field of data science
Valli, L. N., Sujatha, N., Mech, M., & Lokesh, V. S. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202449101026 · Cited by 5 · see citing works
Oyelade, O. N., Wang, H., & Rafferty, K. J. (2025) · ACM Computing Surveys · doi:10.1145/3774628 · Cited by 4 · see citing works
- 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 Vision and Pattern Recognition › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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