InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866912343565271040 |
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| author | Zhou, Zhongyi Jin, Jing Phadnis, Vrushank Yuan, Xiuxiu Jiang, Jun Qian, Xun Wright, Kristen Sherwood, Mark Mayes, Jason Zhou, Jingtao Huang, Yiyi Xu, Zheng Zhang, Yinda Lee, Johnny Olwal, Alex Kim, David Iyengar, Ram Li, Na Du, Ruofei |
| author_facet | Zhou, Zhongyi Jin, Jing Phadnis, Vrushank Yuan, Xiuxiu Jiang, Jun Qian, Xun Wright, Kristen Sherwood, Mark Mayes, Jason Zhou, Jingtao Huang, Yiyi Xu, Zheng Zhang, Yinda Lee, Johnny Olwal, Alex Kim, David Iyengar, Ram Li, Na Du, Ruofei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09672 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs Zhou, Zhongyi Jin, Jing Phadnis, Vrushank Yuan, Xiuxiu Jiang, Jun Qian, Xun Wright, Kristen Sherwood, Mark Mayes, Jason Zhou, Jingtao Huang, Yiyi Xu, Zheng Zhang, Yinda Lee, Johnny Olwal, Alex Kim, David Iyengar, Ram Li, Na Du, Ruofei Human-Computer Interaction Artificial Intelligence 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. |
| title | InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2312.09672 |