| --- |
| pretty_name: Generative Embedding Benchmark |
| license: apache-2.0 |
| task_categories: |
| - visual-question-answering |
| --- |
| |
| # Generative Embedding Benchmark |
|
|
| <div align="center"> |
| <a href="https://arxiv.org/abs/2608.06972"><img src="https://img.shields.io/badge/arXiv-2608.06972-b31b1b.svg" alt="arXiv"></a> |
| <a href="https://github.com/LimitedMouse/Generative-Embedding-Benchmark"><img src="https://img.shields.io/badge/GitHub-Code-lightgrey" alt="GitHub Code"></a> |
| <a href="https://huggingface.co/LimitedMouse/Generative-Embedding-Benchmark-Checkpoints"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Checkpoints-yellow" alt="Hugging Face Checkpoints"></a> |
| </div> |
|
|
| This repository publishes the fixed membership and provenance metadata for the |
| Generative Embedding Benchmark (GEB). GEB contains 1,800 development questions |
| and 900 held-out test questions spanning natural images, scene text, and visual |
| documents. |
|
|
| GEB evaluates how much answer-relevant visual information a dense embedding |
| makes accessible to a generative decoder. The |
| [GitHub repository](https://github.com/LimitedMouse/Generative-Embedding-Benchmark) |
| is the main entry point for installation, training, and evaluation. Official |
| Qwen3-VL-Embedding-2B/8B decoder weights are provided in the |
| [checkpoint repository](https://huggingface.co/LimitedMouse/Generative-Embedding-Benchmark-Checkpoints). |
|
|
| This dataset repository does not redistribute source images, questions, |
| answers, or decoder training data. GEB task definitions load the eight original |
| public evaluation datasets through `lmms-eval` and filter them with `dev.json` |
| or `test.json`. The source datasets remain subject to their own licenses and |
| access requirements. |
|
|
| ## Files |
|
|
| - `dev.json`, `test.json`: compact source-row membership used at runtime. |
| - `manifests/dev/`, `manifests/test/`: categories, provenance identifiers, |
| image hashes, and image-group identifiers used for auditing. |
| - `sources.json`: pinned upstream dataset revisions. |
| - `summary.json`: sampling quotas and aggregate attrition statistics. |
| - `leakage_audit.json`: development/test image-leakage audit. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{li2026generativeembeddingbenchmark, |
| title={Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding?}, |
| author={Yun Li and Biao Yang and Peixi Wu and Yunhao Zhou and Mingzhou Jiang and Wei Yuan and Fan Yang and Wenwu Ou}, |
| year={2026}, |
| eprint={2608.06972}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2608.06972}, |
| } |
| ``` |
|
|
| The Apache-2.0 license applies only to GEB-authored manifests and metadata. It |
| does not alter the terms of the upstream datasets. |
|
|
