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.- Anthology ID:
- 2023.emnlp-demo.31
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Yansong Feng, Els Lefever
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 346–352
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-demo.31
- DOI:
- 10.18653/v1/2023.emnlp-demo.31
- Cite (ACL):
- Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, and Dongmei Zhang. 2023. InsightPilot: An LLM-Empowered Automated Data Exploration System. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 346–352, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- InsightPilot: An LLM-Empowered Automated Data Exploration System (Ma et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.emnlp-demo.31.pdf