InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs

TL;DR

InstructPipe leverages large language models and a code interpreter to enable users, especially novices, to efficiently prototype machine learning pipelines through natural language instructions, simplifying visual programming.

Contribution

This work introduces a novel AI assistant framework combining LLMs and code interpretation for visual ML pipeline creation from text instructions, enhancing usability and creativity.

Findings

Reduces user learning curve in ML pipeline design

Enables open-ended commands for innovative pipeline ideas

Empowers users to prototype pipelines more efficiently

Abstract

Visual programming has the potential of providing novice programmers with a low-code experience to build customized processing pipelines. Existing systems typically require users to build pipelines from scratch, implying that novice users are expected to set up and link appropriate nodes from a blank workspace. In this paper, we introduce InstructPipe, an AI assistant for prototyping machine learning (ML) pipelines with text instructions. We contribute two large language model (LLM) modules and a code interpreter as part of our framework. The LLM modules generate pseudocode for a target pipeline, and the interpreter renders the pipeline in the node-graph editor for further human-AI collaboration. Both technical and user evaluation (N=16) shows that InstructPipe empowers users to streamline their ML pipeline workflow, reduce their learning curve, and leverage open-ended commands to spark innovative ideas.