WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911216405839872 |
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| author | Wang, Kuang-Da Wang, Zhao Shimose, Yotaro Wang, Wei-Yao Takamatsu, Shingo |
| author_facet | Wang, Kuang-Da Wang, Zhao Shimose, Yotaro Wang, Wei-Yao Takamatsu, Shingo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation Wang, Kuang-Da Wang, Zhao Shimose, Yotaro Wang, Wei-Yao Takamatsu, Shingo Artificial Intelligence 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. |
| title | WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.15306 |