Frontend Diffusion: Exploring Intent-Based User Interfaces through Abstract-to-Detailed Task Transitions
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910551808933888 |
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| author | Zhang, Qinshi Hendra, Latisha Besariani Chi, Mohan Ding, Zijian |
| author_facet | Zhang, Qinshi Hendra, Latisha Besariani Chi, Mohan Ding, Zijian |
| contents | The emergence of Generative AI is catalyzing a paradigm shift in user interfaces from command-based to intent-based outcome specification. In this paper, we explore abstract-to-detailed task transitions in the context of frontend code generation as a step towards intent-based user interfaces, aiming to bridge the gap between abstract user intentions and concrete implementations. We introduce Frontend Diffusion, an end-to-end LLM-powered tool that generates high-quality websites from user sketches. The system employs a three-stage task transition process: sketching, writing, and coding. We demonstrate the potential of task transitions to reduce human intervention and communication costs in complex tasks. Our work also opens avenues for exploring similar approaches in other domains, potentially extending to more complex, interdependent tasks such as video production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00778 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Frontend Diffusion: Exploring Intent-Based User Interfaces through Abstract-to-Detailed Task Transitions Zhang, Qinshi Hendra, Latisha Besariani Chi, Mohan Ding, Zijian Human-Computer Interaction Artificial Intelligence Machine Learning The emergence of Generative AI is catalyzing a paradigm shift in user interfaces from command-based to intent-based outcome specification. In this paper, we explore abstract-to-detailed task transitions in the context of frontend code generation as a step towards intent-based user interfaces, aiming to bridge the gap between abstract user intentions and concrete implementations. We introduce Frontend Diffusion, an end-to-end LLM-powered tool that generates high-quality websites from user sketches. The system employs a three-stage task transition process: sketching, writing, and coding. We demonstrate the potential of task transitions to reduce human intervention and communication costs in complex tasks. Our work also opens avenues for exploring similar approaches in other domains, potentially extending to more complex, interdependent tasks such as video production. |
| title | Frontend Diffusion: Exploring Intent-Based User Interfaces through Abstract-to-Detailed Task Transitions |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.00778 |