Envisioning Future Interactive Web Development: Editing Webpage with Natural Language
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909878548692992 |
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| author | Dang, Truong Hai Xiao, Jingyu Huo, Yintong |
| author_facet | Dang, Truong Hai Xiao, Jingyu Huo, Yintong |
| contents | The evolution of web applications relies on iterative code modifications, a process that is traditionally manual and time-consuming. While Large Language Models (LLMs) can generate UI code, their ability to edit existing code from new design requirements (e.g., "center the logo") remains a challenge. This is largely due to the absence of large-scale, high-quality tuning data to align model performance with human expectations. In this paper, we introduce a novel, automated data generation pipeline that uses LLMs to synthesize a high-quality fine-tuning dataset for web editing, named Instruct4Edit. Our approach generates diverse instructions, applies the corresponding code modifications, and performs visual verification to ensure correctness. By fine-tuning models on Instruct4Edit, we demonstrate consistent improvement in translating human intent into precise, structurally coherent, and visually accurate code changes. This work provides a scalable and transparent foundation for natural language based web editing, demonstrating that fine-tuning smaller open-source models can achieve competitive performance with proprietary systems. We release all data, code implementations, and model checkpoints for reproduction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Envisioning Future Interactive Web Development: Editing Webpage with Natural Language Dang, Truong Hai Xiao, Jingyu Huo, Yintong Software Engineering The evolution of web applications relies on iterative code modifications, a process that is traditionally manual and time-consuming. While Large Language Models (LLMs) can generate UI code, their ability to edit existing code from new design requirements (e.g., "center the logo") remains a challenge. This is largely due to the absence of large-scale, high-quality tuning data to align model performance with human expectations. In this paper, we introduce a novel, automated data generation pipeline that uses LLMs to synthesize a high-quality fine-tuning dataset for web editing, named Instruct4Edit. Our approach generates diverse instructions, applies the corresponding code modifications, and performs visual verification to ensure correctness. By fine-tuning models on Instruct4Edit, we demonstrate consistent improvement in translating human intent into precise, structurally coherent, and visually accurate code changes. This work provides a scalable and transparent foundation for natural language based web editing, demonstrating that fine-tuning smaller open-source models can achieve competitive performance with proprietary systems. We release all data, code implementations, and model checkpoints for reproduction. |
| title | Envisioning Future Interactive Web Development: Editing Webpage with Natural Language |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.26516 |