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.

翻译：构建高质量的数据准备程序对于机器学习（ML）的成功至关重要，但这一过程通常耗时且费力。尽管以ChatGPT为代表的大语言模型能够通过自然语言提示与用户交互生成程序，展现出令人印象深刻的能力，但仍存在局限性。具体而言，用户需要提供特定提示以迭代引导ChatGPT改进数据准备程序，这要求用户具备编程、所用数据集及机器学习任务方面的专业知识。此外，一旦程序生成，在不重新开始流程的情况下回溯到先前版本或修改程序并非易事。本文提出ChatPipe——一种旨在促进用户与ChatGPT无缝交互的新型系统。ChatPipe不仅为用户提供下一步数据准备操作的有效建议，还能引导ChatGPT为这些操作生成程序。同时，ChatPipe使用户能够轻松回滚到程序的先前版本，从而支持更高效的实验与测试。我们为ChatPipe开发了网络应用程序，并整理了Kaggle上的多个真实机器学习任务。这些任务可展示ChatPipe的能力，使VLDB与会者能够轻松体验其新颖特性，从而快速编排高质量的数据准备程序。