ViseGPT: Towards Better Alignment of LLM-generated Data Wrangling Scripts and User Prompts

Jiajun Zhu; Xinyu Cheng; Zhongsu Luo; Yunfan Zhou; Xinhuan Shu; Di Weng; Yingcai Wu

doi:10.1145/3746059.3747689

UIST2025顶会

ViseGPT: Towards Better Alignment of LLM-generated Data Wrangling Scripts and User Prompts

Jiajun Zhu, Xinyu Cheng, Zhongsu Luo, Yunfan Zhou, Xinhuan Shu, Di Weng, Yingcai Wu

arXiv DOI 出版方

2025年份

1被引次数

1顶会引用

摘要

Large language models (LLMs) enable the rapid generation of datawrangling scripts based on natural language instructions, but these scripts may not fully adhere to user-specified requirements, necessitating careful inspection and iterative refinement. Existing approaches primarily assist users in understanding script logic and spotting potential issues themselves, rather than providing direct