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

Data wrangling is a time-consuming and challenging task in a data science pipeline. While many tools have been proposed to automate or facilitate data wrangling, they often misinterpret user intent, especially in complex tasks. We propose Dango, a mixed-initiative multi-agent system for data wrangling. 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. We conducted a within-subjects user study (n=38) and demonstrated that Dango’s features can significantly improve intent clarification, accuracy, and efficiency in data wrangling. Furthermore, we demonstrated the generalizability of Dango by applying it to a broader set of data wrangling tasks.