ChatPipe: Orchestrating Data Preparation Program by Optimizing Human-ChatGPT Interactions
This addresses the problem of time-consuming and expertise-heavy data preparation for ML practitioners, though it is incremental as it builds on existing ChatGPT capabilities.
The paper tackles the challenge of orchestrating high-quality data preparation programs for machine learning by introducing ChatPipe, a system that optimizes interactions between users and ChatGPT to generate and refine programs, resulting in more efficient experimentation and testing with features like operation recommendations and version rollback.
Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming. Despite the impressive capabilities of large language models like ChatGPT in generating programs by interacting with users through natural language prompts, there are still limitations. Specifically, a user must provide specific prompts to iteratively guide ChatGPT in improving data preparation programs, which requires a certain level of expertise in programming, the dataset used and the ML task. Moreover, once a program has been generated, it is non-trivial to revisit a previous version or make changes to the program without starting the process over again. In this paper, we present ChatPipe, a novel system designed to facilitate seamless interaction between users and ChatGPT. ChatPipe provides users with effective recommendation on next data preparation operations, and guides ChatGPT to generate program for the operations. Also, ChatPipe enables users to easily roll back to previous versions of the program, which facilitates more efficient experimentation and testing. We have developed a web application for ChatPipe and prepared several real-world ML tasks from Kaggle. These tasks can showcase the capabilities of ChatPipe and enable VLDB attendees to easily experiment with our novel features to rapidly orchestrate a high-quality data preparation program.