---
language:
- zh
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-retrieval
tags:
- text
- retrieval
configs:
- config_name: passages
  data_files:
  - split: test
    path: passages/test*
- config_name: queries
  data_files:
  - split: test
    path: queries/test*
---

The dataset **CapRetrieval** introduced in the EMNLP 2025 Finding paper: [[Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings](https://arxiv.org/abs/2506.08592)].

**CapRetrieval** is in Chinese; the according English version is available at [CapRetrievalEn](https://huggingface.co/datasets/lxucs/CapRetrievalEn), sharing the same queries, passages and labels.

### Introduction

CapRetrieval evaluates the fine-grained embedding matching (dense passage retrieval), 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
@inproceedings{xu-etal-2025-dense,
    title = "Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings",
    author = "Xu, Liyan and Su, Zhenlin and Yu, Mo and Li, Jiangnan and Meng, Fandong and Zhou, Jie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics"
}
```