| --- |
| pretty_name: Pattern2Code |
| license: odc-by |
| task_categories: |
| - image-to-text |
| - text-generation |
| language: |
| - en |
| tags: |
| - screenshot-to-code |
| - multimodal |
| - html |
| - css |
| - visual-grounding |
| - benchmark |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| default: true |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| dataset_info: |
| features: |
| - name: standard_image |
| dtype: image |
| - name: noise_image |
| dtype: image |
| - name: example_id |
| dtype: string |
| - name: source_id |
| dtype: string |
| - name: pattern_family |
| dtype: string |
| - name: pattern_name |
| dtype: string |
| - name: attribute_name |
| dtype: string |
| - name: attribute_token |
| dtype: string |
| - name: position |
| dtype: string |
| - name: target_value |
| dtype: string |
| - name: target_value_from_filename |
| dtype: string |
| - name: bias_value |
| dtype: string |
| - name: pattern_description |
| dtype: string |
| - name: element_description |
| dtype: string |
| - name: code_snippet_line |
| dtype: string |
| - name: code_snippet_context |
| dtype: string |
| - name: html_path |
| dtype: string |
| - name: standard_image_path |
| dtype: string |
| - name: noise_image_path |
| dtype: string |
| - name: modified_index |
| dtype: int32 |
| splits: |
| - name: train |
| num_examples: 720 |
| --- |
| |
| # Pattern2Code |
|
|
| **Benchmark for the ASE 2026 paper *"Pattern Over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation."*** |
|
|
| Project page: https://pattern2code.github.io/ · Artifact (Zenodo): https://doi.org/10.5281/zenodo.19341952 |
|
|
| Pattern2Code measures *visual pattern-completion bias* in screenshot-to-code |
| generation: when a webpage contains a repeated UI pattern and exactly **one** |
| element deviates from it, does a multimodal LLM write the code that matches the |
| **pixels**, or the code that matches the **pattern**? |
|
|
| Each example pairs a rendered screenshot with the element's HTML snippet in which |
| the deviating CSS value is masked as `__`. The model must recover the masked |
| value from the image. |
|
|
| ## Key results (5 frontier MLLMs) |
|
|
| | Family | Mean accuracy | Mean bias rate | |
| |---|---|---| |
| | Card patterns (`width`) | 21.17% | **69.78%** | |
| | Text patterns (`font-size`) | 7.89% | **80.22%** | |
|
|
| *Bias rate* = fraction of predictions equal to the repeated baseline `100%`. |
|
|
| ## Contents |
|
|
| - **720** perturbed instances = 30 webpages × 2 pattern families × 3 positions × 4 magnitudes |
| - **360** structural-card (`width`) + **360** text-style (`font-size`) examples |
| - **1,440** screenshots — every instance rendered under 2 conditions (`standard_image`, `noise_image`) |
| - Perturbation magnitudes: `80%`, `90%`, `110%`, `120%`; unperturbed baseline is `100%` |
|
|
| ## Layout |
|
|
| ```text |
| data/train-*.parquet # the dataset: one row per instance, screenshots embedded |
| data/examples.jsonl # the same rows as raw JSON (image columns are paths only) |
| metadata/dataset_info.json # summary statistics |
| metadata/validation_report.json |
| html/{cards,texts}/ # perturbed HTML |
| images/{cards,texts}/{standard,noise}/ # the screenshots as loose PNG files |
| ``` |
|
|
| The Parquet files carry the screenshots inline, so both image columns render |
| directly in the dataset viewer and decode to `PIL.Image` with no extra setup. |
| The loose PNGs under `images/` are the same files, kept for anyone who wants to |
| work with them outside the `datasets` library. |
|
|
| ## Row schema |
|
|
| | Field | Description | |
| |---|---| |
| | `standard_image` | **Image** — the rendered screenshot | |
| | `noise_image` | **Image** — the same page with eight opaque rectangles overlaid | |
| | `example_id` | Unique instance id | |
| | `source_id` | Seed webpage id (Design2Code) | |
| | `pattern_family` | `structural_card` or `text_style` | |
| | `pattern_name`, `pattern_description`, `element_description` | The repeated pattern being probed | |
| | `attribute_name` | Masked CSS property (`width` or `font_size`) | |
| | `attribute_token` | The mask token (`__`) | |
| | `position` | Which element was perturbed: `first`, `mid`, `last` | |
| | `modified_index` | Index of the perturbed element within the pattern | |
| | `target_value` | **Ground truth** — the perturbed value (e.g. `120%`) | |
| | `bias_value` | The pattern-consistent baseline (always `100%`) | |
| | `code_snippet_line` | The masked element's HTML | |
| | `code_snippet_context` | Masked element plus its sibling elements (model input) | |
| | `html_path` | Relative path to the full perturbed HTML | |
| | `standard_image_path` / `noise_image_path` | Relative paths to the two screenshot files | |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("knguyennguyen/pattern2code", split="train") |
| |
| ex = ds[0] |
| ex["standard_image"] # PIL.Image — already decoded, no cast_column needed |
| ex["noise_image"] # PIL.Image |
| print(ex["attribute_name"], ex["target_value"], "vs baseline", ex["bias_value"]) |
| print(ex["code_snippet_context"]) |
| ``` |
|
|
| ## Evaluation protocol |
|
|
| Prompt the model with `standard_image` (or `noise_image`) plus `code_snippet_context`: |
|
|
| ``` |
| You are doing a visual code fill-in-the-blank task. Given the webpage screenshot |
| and HTML snippet below, fill the blank token __ with ONLY the missing CSS/HTML |
| value. The goal is to predict the masked value to recreate the {pattern} in the |
| webpage design. Return the final answer in JSON format: {"answer":"<value>"}. |
| The blank must be a percentage value divisible by 10. |
| ``` |
|
|
| Score each prediction into three mutually exclusive outcomes: |
|
|
| - **Correct** — equals `target_value` |
| - **Bias-aligned** — equals `bias_value` (`100%`) |
| - **Other error** — neither |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{nguyen2026pattern, |
| title = {Pattern Over Pixels: Measuring Pattern Completion Bias in |
| Multimodal Code Generation}, |
| author = {Nguyen, Khai-Nguyen and Chaparro, Oscar and Mastropaolo, Antonio}, |
| booktitle = {Proceedings of the 41st IEEE/ACM International Conference on |
| Automated Software Engineering (ASE)}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## License & provenance |
|
|
| The seed webpages come from the |
| [Design2Code](https://github.com/NoviScl/Design2Code) dataset, which is released |
| under the **Open Data Commons Attribution License (ODC-By)** as it is built on |
| the C4 corpus (also ODC-By). All perturbed HTML and screenshots here are |
| derivative works and are redistributed under **ODC-By** with attribution. |
| Copyright in the original website content remains with the respective site |
| owners; the material is shared for research and reproducibility purposes. |
|
|
