Update README.md

README.md

@@ -56,7 +56,7 @@ size_categories:
 - 1K<n<10K
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 ## Introduction
-This repo contains the LongEmbed benchmark proposed in the paper [LongEmbed: Extending Embedding Models for Long Context Retrieval](). Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li, arxiv 2024.04. Github Repo for LongEmbed: https://github.com/dwzhu-pku/LongEmbed.
+This repo contains the LongEmbed benchmark proposed in the paper [LongEmbed: Extending Embedding Models for Long Context Retrieval](https://arxiv.org/abs/2404.12096). Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li, arxiv 2024.04. Github Repo for LongEmbed: https://github.com/dwzhu-pku/LongEmbed.
 
 **LongEmbed** is designed to benchmark long context retrieval. It includes two synthetic tasks and four real-world tasks, featuring documents of varying lengths and dispersed target information. It has been integrated into [MTEB](https://github.com/embeddings-benchmark/mteb) for the convenience of evaluation.
 
@@ -137,4 +137,10 @@ We also include two synthetic tasks, namely needle and passkey retrieval. The fo
 If you find our paper helpful, please consider cite as follows:
 
 ```
+@article{zhu2024longembed,
+  title={LongEmbed: Extending Embedding Models for Long Context Retrieval},
+  author={Zhu, Dawei and Wang, Liang and Yang, Nan and Song, Yifan and Wu, Wenhao and Wei, Furu and Li, Sujian},
+  journal={arXiv preprint arXiv:2404.12096},
+  year={2024}
+}
 ```