Скачать или смотреть Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Progra...

Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Progra...
Zhongyi Zhou, Jing Jin, Vrushank Phadnis, Xiuxiu Yuan, Jun Jiang, Xun Qian, Jingtao Zhou, Yiyi Huang, Zheng Xu, Yinda Zhang, Kristen Wright, Jason Mayes, Mark Sherwood, Johnny Lee, Alex Olwal, David Kim, Ram Iyengar, Na Li, Ruofei Du

CHI 2024: The ACM CHI Conference on Human Factors in Computing Systems
Session: Interactivity

Foundational multi-modal models have democratized AI access, yet the construction of complex, customizable machine learning pipelines by novice users remains a grand challenge. This paper demonstrates a visual programming system that allows novices to rapidly prototype multimodal AI pipelines. We first conducted a formative study with 58 contributors and collected 236 proposals of multimodal AI pipelines that served various practical needs. We then distilled our findings into a design matrix of primitive nodes for prototyping multimodal AI visual programming pipelines, and implemented a system with 65 nodes. To support users' rapid prototyping experience, we built InstructPipe, an AI assistant based on large language models (LLMs) that allows users to generate a pipeline by writing text-based instructions. We believe InstructPipe enhances novice users onboarding experience of visual programming and the controllability of LLMs by offering non-experts a platform to easily update the generation.

Web:: https://programs.sigchi.org/chi/2024/...

Pre-recorded video presentations for Interactivity Exhibits at CHI 2024