Back to Search Start Over

InstructPipe: Building Visual Programming Pipelines with Human Instructions

Authors :
Zhou, Zhongyi
Jin, Jing
Phadnis, Vrushank
Yuan, Xiuxiu
Jiang, Jun
Qian, Xun
Zhou, Jingtao
Huang, Yiyi
Xu, Zheng
Zhang, Yinda
Wright, Kristen
Mayes, Jason
Sherwood, Mark
Lee, Johnny
Olwal, Alex
Kim, David
Iyengar, Ram
Li, Na
Du, Ruofei
Publication Year :
2023

Abstract

Visual programming provides beginner-level programmers with a coding-free experience to build their customized pipelines. Existing systems require users to build a pipeline entirely from scratch, implying that novice users need to set up and link appropriate nodes all by themselves, starting from a blank workspace. We present InstructPipe, an AI assistant that enables users to start prototyping machine learning (ML) pipelines with text instructions. We designed two LLM modules and a code interpreter to execute our solution. LLM modules generate pseudocode of a target pipeline, and the interpreter renders a pipeline in the node-graph editor for further human-AI collaboration. Technical evaluations reveal that InstructPipe reduces user interactions by 81.1% compared to traditional methods. Our user study (N=16) showed that InstructPipe empowers novice users to streamline their workflow in creating desired ML pipelines, reduce their learning curve, and spark innovative ideas with open-ended commands.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2312.09672
Document Type :
Working Paper