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---
license: apache-2.0
task_categories:
- text-retrieval
language:
- zh
tags:
- text
- retrieval
size_categories:
- 1K<n<10K
configs:
- config_name: passages
data_files:
- split: test
path: passages/test*
- config_name: queries
data_files:
- split: test
path: queries/test*
---
Dataset **CapRetrieval** introduced in [Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings](https://arxiv.org/abs/2506.08592).
CapRetrieval evaluates the fine-grained embedding matching (dense passage retrieval) in Chinese, tailored towards a practical image search scenario:
- Candidate passages are image captions, and queries are short phrases of entities or events reflected in captions.
- Overall, the dataset comprises seemingly simple queries and captions; however, text encoders are shown limitations resolving these cases.
- Evaluation results call for attention on embedding training strategies with different **granularity**.
### Format
CapRetrieval follows the same retrieval task format as in MTEB, with relevance labels in [0,1,2] for each pair.
Note that unlike prior datasets, we annotate full labels for each query-passage pair (1.3 million pairs), minimizing false negatives for more accurate evaluation.
A small amount of queries do not have any relevant captions; they are excluded in computation of retrieval metrics (e.g. nDCG), but can be useful for other analysis, e.g. in classification setting.
### Evaluation
Please see the evaluation script and results at https://github.com/lxucs/CapRetrieval.
| Type | Model | nDCG@10 |
|----------|-------------------------|-----------|
| **BM25** | Basic BM25 | 66.54 |
| **0.1B** | bge-base-zh-v1.5 | 78.86 |
| | gte-multilingual-base | 79.67 |
| | multilingual-e5-base | 76.33 |
| **0.3B** | bge-large-zh-v1.5 | 79.15 |
| | multilingual-e5-large | 81.01 |
| | Conan-embedding-v1 | 77.04 |
| **0.6B** | Qwen3-Embedding-0.6B | 81.04 |
| **>1B** | gte-Qwen2-1.5B-instruct | 77.35 |
| | gte-Qwen2-7B-instruct | **86.55** |
| | e5-mistral-7b-instruct | 76.40 |
| | Qwen3-Embedding-8B | 84.61 |
| | | |
| Trained | Out-of-Domain | 87.23 |
| | In-Domain | 91.83 |
The trained models (based on `bge-base-zh-v1.5`) are trained with queries by our data generation strategies described in the paper. The in-domain model can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1l2pvELMQPKjhAasNGaY7d14jMK0iCRhj).
### Citation
```bibtex
@misc{xu2025denseretrieversfailsimple,
title={Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings},
author={Liyan Xu and Zhenlin Su and Mo Yu and Jiangnan Li and Fandong Meng and Jie Zhou},
year={2025},
eprint={2506.08592},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.08592},
}
```
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