Update README.md

Author: lupantech

README.md

@@ -10,7 +10,7 @@ This repo proposes **LLaMA-Adapter (V2)**, a lightweight adaption method for fin
 Try out the web demo 🤗 of LLaMA-Adapter: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/csuhan/LLaMA-Adapter)
 
 ## News
-- **[2023.04.30]** We are noticed that GPT-4 evaluation has a strong positional bias in favor of the first response. We will soon update the paper to reveal the position bias. GPT-4 evaluation also shows a potential to automate chatbot assessment, but still requires further research. Great thanks to [Canwen Xu](https://scholar.google.com/citations?user=oopKCDMAAAAJ&hl=en).
+- **[2023.04.30]** We noticed that GPT-4 evaluation has a strong positional bias in favor of the first response. We will soon update the paper to reveal the position bias. GPT-4 evaluation also shows a potential to automate chatbot assessment, but still requires further research. Great thanks to [Canwen Xu](https://scholar.google.com/citations?user=oopKCDMAAAAJ&hl=en).
 - **[2023.04.30]** The technical report for **LLaMA-Adapter V2** is released at [preprint](https://arxiv.org/abs/2304.15010).
 - **[2023.04.28]** 🔥🔥 We release **LLaMA-Adapter V2 (65B)**, a multi-modal instruction model! Check out our [demos](#demos) and [code](https://github.com/ZrrSkywalker/LLaMA-Adapter/tree/main/llama_adapter_v2_chat65b)!
 - **[2023.04.15]** The **Training Code** for LLaMA-Adapter (7B) can now be found in [alpaca finetune v1](https://github.com/ZrrSkywalker/LLaMA-Adapter/tree/main/alpaca_finetuning_v1). 📌
@@ -331,12 +331,22 @@ for (let i = 1; i <= 100; i++) {
 ## Citation
 If you find our LLaMA-Adapter code and paper useful, please kindly cite:
 ```bash
-@article{llamaadapter2023,
+@article{zhang2023llamaadapter,
   title = {LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention},
   author={Zhang, Renrui and Han, Jiaming and Zhou, Aojun and Hu, Xiangfei and Yan, Shilin and Lu, Pan and Li, Hongsheng and Gao, Peng and Qiao Yu},
   journal={arXiv preprint arXiv:2303.16199},
   year={2023}
 }
+  
+If you find our LLaMA-Adapter V2 code and paper useful, please kindly cite:
+```bash
+@article{gao2023llamaadapterv2,
+  title = {LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model},
+  author={Gao, Peng and Han, Jiaming and Zhang, Renrui and Lin, Ziyi and Geng, Shijie and Zhou, Aojun and Zhang, Wei and Lu, Pan and He, Conghui and Yue, Xiangyu and Li, Hongsheng and Qiao Yu},
+  journal={arXiv preprint arXiv:2304.15010},
+  year={2023}
+}
+}
 ```
 
 ## Acknowledgement