InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs
Zhongyi Zhou, Jing Jin, Vrushank Phadnis, Xiuxiu Yuan, Jun Jiang, Xun Qian, Kristen Wright, Mark Sherwood, Jason Mayes, Jingtao Zhou, Yiyi Huang, Zheng Xu
摘要
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 workfow, reduce their learning curve, and leverage open-ended commands to spark innovative ideas.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper7
- LLMR: Real-time Prompting of Interactive Worlds using Large Language ModelsFernanda De La Torre, Cathy Mengying Fang, Han Huang, Andrzej Banburski-Fahey 等CHI 2024 · 被引用 124 次
- Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System DesignYongquan 'Owen' Hu, Jingyu Tang, Xinya Gong, Zhongyi Zhou 等CHI 2025 · 被引用 37 次
- ChainBuddy: An AI-assisted Agent System for Generating LLM PipelinesJingyue Zhang, Ian ArawjoCHI 2025 · 被引用 13 次
- Interaction-Augmented Instruction: Modeling the Synergy of Prompts and Interactions in Human-GenAI CollaborationLeixian Shen, Yifang Wang, Huamin Qu, Xing Xie 等CHI 2026 · 被引用 3 次
- (De)composing Craft: An Elementary Grammar for Sharing Expertise in Craft WorkflowsRitik Batra, Lydia Kim, Ilan Mandel, Amritansh Kwatra 等CSCW 2026 · 被引用 2 次
它引用的顶会 Paper42
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma 等NeurIPS 2022 · 被引用 22,562 次
- Toolformer: Language Models Can Teach Themselves to Use ToolsTimo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu 等NeurIPS 2023 · 被引用 5,989 次
- Generative Agents: Interactive Simulacra of Human BehaviorJoon Sung Park, Joseph C. O'Brien, Carrie Jun Cai, Meredith Ringel Morris 等UIST 2023 · 被引用 1,882 次
- Calibrate Before Use: Improving Few-shot Performance of Language ModelsZihao Zhao, Eric Wallace, Shi Feng, Dan Klein 等ICML 2021 · 被引用 1,843 次
相关 Paper
- An Exploratory Study of ML Sketches and Visual Code AssistantsLuís F. Gomes, Vincent J. Hellendoorn, Jonathan Aldrich, Rui AbreuICSE 2025 · 被引用 2 次
- LLaRA: Supercharging Robot Learning Data for Vision-Language PolicyXiang Li, Cristina Mata, Jongwoo Park, Kumara Kahatapitiya 等ICLR 2025 · 被引用 2 次
- CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language ProgrammingLi Feng, Ryan Yen, Yuzhe You, Mingming Fan 等CHI 2024 · 被引用 28 次
- Guiding Instruction-based Image Editing via Multimodal Large Language ModelsTsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang 等ICLR 2024 · 被引用 173 次
- AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLPatara Trirat, Wonyong Jeong, Sung Ju HwangICML 2025
