# Envisioning Future Interactive Web Development: Editing Webpage with Natural Language

arXiv: https://arxiv.org/abs/2510.26516 · HTML: https://mlsanity.com/paper/2510.26516/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection](https://mlsanity.com/paper/2603.00155/) (2026)
- [Omni2Web: Benchmarking Audiovisual Website Development](https://mlsanity.com/paper/2609.23417/) (2026)
- [CodeShrink: Adaptive Visual Compression for Efficient Multimodal Code Understanding](https://mlsanity.com/paper/2607.29637/) (2026)
- [TeleGen: Improving LLM-Based Web Application Generation via Runtime Telemetry](https://mlsanity.com/paper/2610.04981/) (2026)

## Abstract (arXiv)

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.