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README.md

@@ -13,7 +13,7 @@ base_model:
 
 > **GitHub repository** for exploring the source code and additional resources: https://github.com/wangkevin02/USP
 
-The **Profile Generator** is a model designed to extract and generate detailed user profiles from given dialogues, particularly those simulated by our User Simulator for Reinforcement Learning with Cycle Consistency (RLCC) as described in [our paper](https://tongyi.aliyun.com/qianwen/?sessionId=ea3bbcf36a2346a0a7819b06fcb36a1c#). Built upon the **LLaMA-3-Instruct** architecture, this model has been fine-tuned through knowledge distillation of the user profile generation capabilities of **GPT-4o**. As demonstrated in the table below, the distilled Profile Generator achieves dialogue profile consistency (DPC) nearly equivalent to GPT-4o.
+The **Profile Generator** is a model designed to extract and generate detailed user profiles from given dialogues, particularly those simulated by our User Simulator for Reinforcement Learning with Cycle Consistency (RLCC) as described in [our paper](https://arxiv.org/pdf/2502.18968). Built upon the **LLaMA-3-Instruct** architecture, this model has been fine-tuned through knowledge distillation of the user profile generation capabilities of **GPT-4o**. As demonstrated in the table below, the distilled Profile Generator achieves dialogue profile consistency (DPC) nearly equivalent to GPT-4o.
 
 | Dataset   | Profile Source | DP.P  | Avg DP.P # Fact | DPR   | Avg DPR # Fact | DPC   | SC Val.Score |
 | --------- | -------------- | ----- | --------------- | ----- | -------------- | ----- | ------------ |
@@ -173,5 +173,13 @@ print(f"profile:{profile}")
 If you find this model useful, please cite:
 
 ```plaintext
-[Authors], "[Paper Title]," [Venue], [Year], [URL or DOI].
+@misc{wang2025knowbettermodelinghumanlike,
+      title={Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profiles}, 
+      author={Kuang Wang and Xianfei Li and Shenghao Yang and Li Zhou and Feng Jiang and Haizhou Li},
+      year={2025},
+      eprint={2502.18968},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.18968}, 
+}
 ```