Update README.md

README.md

@@ -7,7 +7,7 @@ license: apache-2.0
 </div>
 
 <p align="center">
-   <a href="https://arxiv.org/abs/">Arxiv</a>&nbsp | &nbsp<a href="https://github.com/YZ-Cai/ERank">Github</a>
+   <a href="https://arxiv.org/abs/2509.00520">Arxiv</a>
 </p>
 
 ## Introduction
@@ -74,7 +74,7 @@ Since ERank is a pointwise reranker, it has low latency compared with listwise m
 <img src="./assets/latency.png" width=400px>
 </div>
 
-For more details, please refer to our [Paper](https://arxiv.org/abs/).
+For more details, please refer to our [Paper](https://arxiv.org/abs/2509.00520).
 
 ## Usage
 
@@ -86,9 +86,9 @@ from examples.ERank_vLLM import ERank_vLLM
 from examples.utils import hybrid_scores
 
 # select a model
-model_name_or_path = "Alibaba-NLP/ERank-4B"
+# model_name_or_path = "Alibaba-NLP/ERank-4B"
 # model_name_or_path = "Alibaba-NLP/ERank-14B"
-# model_name_or_path = "Alibaba-NLP/ERank-32B"
+model_name_or_path = "Alibaba-NLP/ERank-32B"
 
 # use vLLM or Transformer
 # reranker = ERank_Transformer(model_name_or_path)
@@ -130,5 +130,13 @@ Please refer to the `examples` directory for details, in which we also provide t
 If you find our work helpful, feel free to give us a cite.
 
 ```
-
+@misc{ERank,
+      title={ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking}, 
+      author={Yuzheng Cai and Yanzhao Zhang and Dingkun Long and Mingxin Li and Pengjun Xie and Weiguo Zheng},
+      year={2025},
+      eprint={2509.00520},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2509.00520}, 
+}
 ```