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  license: cc-by-4.0
 
 
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  ---
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  # 🧠 CC-HARD: A Challenging Dataset for Design-to-Code Generation
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  [📄 Paper on arXiv](https://arxiv.org/pdf/2508.03560)
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  [📄 Paper on ACM](https://arxiv.org/pdf/2508.03560)
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- **CC-HARD** (Common Crawl – Hard) is a challenging benchmark dataset introduced in the KDD 2025 paper *[LaTCoder: Converting Webpage Design to Code with Layout-as-Thought](https://arxiv.org/pdf/2508.03560)*.
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  It was specifically designed to evaluate layout fidelity in **webpage design-to-code generation**.
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  The dataset consists of **128 webpage screenshots** and their corresponding **HTML/CSS code**, manually curated from the Common Crawl corpus. Unlike prior datasets, CC-HARD emphasizes:
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  - `image`: A high-resolution PNG screenshot of a real-world webpage design
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  - `text`: The corresponding HTML/CSS code used to render that design
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- ---
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-
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- ## 📊 Dataset Comparison: CC-HARD vs. Design2Code-HARD
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  We compare **CC-HARD** with **Design2Code-HARD** dataset across multiple structural and content dimensions.
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  The following table summarizes the key statistics, as reported in the *LaTCoder* paper (Table 2):
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  | Avg. Unique Tags | 23 ± 5 | 27 ± 5 |
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- ### 🔍 Key Differences and Analysis
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  As discussed in the paper:
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  As a result, models that perform well on Design2Code-HARD often struggle on CC-HARD — a trend clearly shown in the benchmark results.
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  This highlights the **increased layout sensitivity and real-world difficulty** embedded in CC-HARD, making it a more suitable testbed for evaluating layout-aware design-to-code systems.
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  ## 🧾 Citation
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  keywords = {code generation, design to code, ui automation},
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  location = {Toronto ON, Canada},
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  series = {KDD '25}
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- }
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-
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-
 
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  ---
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  license: cc-by-4.0
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+ task_categories:
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+ - text-generation
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  ---
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  # 🧠 CC-HARD: A Challenging Dataset for Design-to-Code Generation
 
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  [📄 Paper on arXiv](https://arxiv.org/pdf/2508.03560)
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  [📄 Paper on ACM](https://arxiv.org/pdf/2508.03560)
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+ **CC-HARD** is a challenging benchmark dataset introduced in the KDD 2025 paper *LaTCoder: Converting Webpage Design to Code with Layout-as-Thought*.
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  It was specifically designed to evaluate layout fidelity in **webpage design-to-code generation**.
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  The dataset consists of **128 webpage screenshots** and their corresponding **HTML/CSS code**, manually curated from the Common Crawl corpus. Unlike prior datasets, CC-HARD emphasizes:
 
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  - `image`: A high-resolution PNG screenshot of a real-world webpage design
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  - `text`: The corresponding HTML/CSS code used to render that design
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+ ## Dataset Comparison: CC-HARD vs. Design2Code-HARD
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  We compare **CC-HARD** with **Design2Code-HARD** dataset across multiple structural and content dimensions.
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  The following table summarizes the key statistics, as reported in the *LaTCoder* paper (Table 2):
 
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  | Avg. Unique Tags | 23 ± 5 | 27 ± 5 |
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+ ### Key Differences and Analysis
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  As discussed in the paper:
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  As a result, models that perform well on Design2Code-HARD often struggle on CC-HARD — a trend clearly shown in the benchmark results.
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  This highlights the **increased layout sensitivity and real-world difficulty** embedded in CC-HARD, making it a more suitable testbed for evaluating layout-aware design-to-code systems.
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+ ---
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  ## 🧾 Citation
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  keywords = {code generation, design to code, ui automation},
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  location = {Toronto ON, Canada},
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  series = {KDD '25}
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+ }