WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation

Kuang-Da Wang; Zhao Wang; Yotaro Shimose; Wei-Yao Wang; Shingo Takamatsu

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arXiv · 2510.15306

WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation

Kuang-Da Wang·Zhao Wang·Yotaro Shimose·Wei-Yao Wang·Shingo Takamatsu

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Abstract

Witnessed by the recent advancements on leveraging LLM for coding and multimodal understanding, we present WebGen-V, a new benchmark and framework for instruction-to-HTML generation that enhances both data quality and evaluation granularity. WebGen-V contributes three key innovations: (1) an unbounded and extensible agentic crawling framework that continuously collects real-world webpages and can leveraged to augment existing benchmarks; (2) a structured, section-wise data representation that integrates metadata, localized UI screenshots, and JSON-formatted text and image assets, explicit alignment between content, layout, and visual components for detailed multimodal supervision; and (3) a section-level multimodal evaluation protocol aligning text, layout, and visuals for high-granularity assessment. Experiments with state-of-the-art LLMs and ablation studies validate the effectiveness of our structured data and section-wise evaluation, as well as the contribution of each component. To the best of our knowledge, WebGen-V is the first work to enable high-granularity agentic crawling and evaluation for instruction-to-HTML generation, providing a unified pipeline from real-world data acquisition and webpage generation to structured multimodal assessment.

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Kuang-Da Wang, Zhao Wang, Yotaro Shimose, Wei-Yao Wang, Shingo Takamatsu. 2025-10-17. WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation. https://arxiv.org/abs/2510.15306

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