Dango: A Mixed-Initiative Data Wrangling System using Large Language Model

要旨

Data wrangling is a time-consuming and challenging task in the early stages of a data science pipeline. However, existing tools often fail to effectively interpret user intent. We propose Dango, a mixed-initiative multi-agent system that helps users generate data wrangling scripts. Compared to existing tools, Dango enhances user communication of intent by: (1) allowing users to demonstrate on multiple tables and use natural language prompts in a conversation interface, (2) enabling users to clarify their intent by answering LLM-posed multiple-choice clarification questions, and (3) providing multiple forms of feedback such as step-by-step NL explanations and data provenance to help users evaluate the data wrangling scripts. In a within-subjects, think-aloud study (n=38), the results show that Dango's features can significantly improve intent clarification, accuracy, and efficiency in data wrangling tasks.

著者
Wei-Hao Chen
Purdue University, West Lafayette, Indiana, United States
Weixi Tong
Huazhong University of Science and Technology, Wuhan, China
Amanda Case
University of Iowa, Iowa City, Iowa, United States
Tianyi Zhang
Purdue University, West Lafayette, Indiana, United States
DOI

10.1145/3706598.3714135

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714135

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Engaging with Data

Annex Hall F206
7 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
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