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In this paper, we examine the application of LLMs in text-to-visualization (text-to-vis). The advancement of natural language processing technologies has made natural language interfaces more accessible and intuitive for visualizing tabular data. However, despite utilizing advanced neural network architectures, current methods such as Seq2Vis, ncNet, and RGVisNet for transforming natural language queries into DV commands still underperform, indicating significant room for improvement. In this paper, we introduce <jats:sc>Prompt4Vis<\/jats:sc>, a novel framework that leverages LLMs and In-context learning to enhance the generation of data visualizations from natural language. Given that In-context learning\u2019s effectiveness is highly dependent on the selection of examples, it is critical to optimize this aspect. Additionally, encoding the full database schema of a query is not only costly but can also lead to inaccuracies. This framework includes two main components: (1) an example mining module that identifies highly effective examples to enhance In-context learning capabilities for text-to-vis applications, and (2) a schema filtering module designed to streamline database schemas. Comprehensive testing on the <jats:italic>NVBench<\/jats:italic> dataset has shown that <jats:sc>Prompt4Vis<\/jats:sc> significantly outperforms the current state-of-the-art model, RGVisNet, by approximately 35.9% on development sets and 71.3% on test sets. To the best of our knowledge, <jats:sc>Prompt4Vis<\/jats:sc> is the first framework to incorporate In-context learning for enhancing text-to-vis, marking a pioneering step in the domain.<\/jats:p>","DOI":"10.1007\/s00778-025-00912-0","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T09:16:25Z","timestamp":1746177385000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["prompt4vis: prompting large language models with example mining for tabular data visualization"],"prefix":"10.1007","volume":"34","author":[{"given":"Shuaimin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanfeng","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunze","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3306-9317","authenticated-orcid":false,"given":"Chen Jason","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,2]]},"reference":[{"key":"912_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. 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