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--- |
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license: apache-2.0 |
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task_categories: |
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- text-retrieval |
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language: |
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- zh |
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tags: |
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- text |
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- retrieval |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: passages |
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data_files: |
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- split: test |
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path: passages/test* |
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- config_name: queries |
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data_files: |
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- split: test |
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path: queries/test* |
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--- |
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Dataset **CapRetrieval** introduced in [Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings](https://arxiv.org/abs/2506.08592). |
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CapRetrieval evaluates the fine-grained embedding matching (dense passage retrieval) in Chinese, tailored towards a practical image search scenario: |
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- Candidate passages are image captions, and queries are short phrases of entities or events reflected in captions. |
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- Overall, the dataset comprises seemingly simple queries and captions; however, text encoders are shown limitations resolving these cases. |
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- Evaluation results call for attention on embedding training strategies with different **granularity**. |
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### Format |
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CapRetrieval follows the same retrieval task format as in MTEB, with relevance labels in [0,1,2] for each pair. |
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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. |
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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. |
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### Evaluation |
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Please see the evaluation script and results at https://github.com/lxucs/CapRetrieval. |
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| Type | Model | nDCG@10 | |
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|----------|-------------------------|-----------| |
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| **BM25** | Basic BM25 | 66.54 | |
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| **0.1B** | bge-base-zh-v1.5 | 78.86 | |
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| | gte-multilingual-base | 79.67 | |
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| | multilingual-e5-base | 76.33 | |
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| **0.3B** | bge-large-zh-v1.5 | 79.15 | |
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| | multilingual-e5-large | 81.01 | |
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| | Conan-embedding-v1 | 77.04 | |
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| **0.6B** | Qwen3-Embedding-0.6B | 81.04 | |
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| **>1B** | gte-Qwen2-1.5B-instruct | 77.35 | |
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| | gte-Qwen2-7B-instruct | **86.55** | |
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| | e5-mistral-7b-instruct | 76.40 | |
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| | Qwen3-Embedding-8B | 84.61 | |
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| | | | |
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| Trained | Out-of-Domain | 87.23 | |
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| | In-Domain | 91.83 | |
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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). |
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### Citation |
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```bibtex |
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@misc{xu2025denseretrieversfailsimple, |
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title={Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings}, |
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author={Liyan Xu and Zhenlin Su and Mo Yu and Jiangnan Li and Fandong Meng and Jie Zhou}, |
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year={2025}, |
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eprint={2506.08592}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2506.08592}, |
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} |
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``` |
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