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