Update README.md

README.md

@@ -18,4 +18,60 @@ configs:
     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}, 
+}
+```
+