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@@ -18,4 +18,60 @@ configs:
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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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  data_files:
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  - split: test
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  path: queries/test*
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+ ---
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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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+
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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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+
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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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+
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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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+
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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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+
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+ ### Citation
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+
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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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+