{"ID":2848063,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.26516","arxiv_id":"2510.26516","title":"Envisioning Future Interactive Web Development: Editing Webpage with Natural Language","abstract":"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.","short_abstract":"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 du...","url_abs":"https://arxiv.org/abs/2510.26516","url_pdf":"https://arxiv.org/pdf/2510.26516v1","authors":"[\"Truong Hai Dang\",\"Jingyu Xiao\",\"Yintong Huo\"]","published":"2025-10-30T14:09:50Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}