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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.
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