@inproceedings{ma-etal-2023-insightpilot,
    title = "{I}nsight{P}ilot: An {LLM}-Empowered Automated Data Exploration System",
    author = "Ma, Pingchuan  and
      Ding, Rui  and
      Wang, Shuai  and
      Han, Shi  and
      Zhang, Dongmei",
    editor = "Feng, Yansong  and
      Lefever, Els",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.emnlp-demo.31/",
    doi = "10.18653/v1/2023.emnlp-demo.31",
    pages = "346--352",
    abstract = "Exploring data is crucial in data analysis, as it helps users understand and interpret the data more effectively. However, performing effective data exploration requires in-depth knowledge of the dataset, the user intent and expertise in data analysis techniques. Not being familiar with either can create obstacles that make the process time-consuming and overwhelming. To address this issue, we introduce InsightPilot, an LLM (Large Language Model)-based, automated data exploration system designed to simplify the data exploration process. InsightPilot features a set of carefully designed analysis actions that streamline the data exploration process. Given a natural language question, InsightPilot collaborates with the LLM to issue a sequence of analysis actions, explore the data and generate insights. We demonstrate the effectiveness of InsightPilot in a user study and a case study, showing how it can help users gain valuable insights from their datasets."
}