Update README.md

README.md

@@ -10,7 +10,7 @@
 **High-Performance In-Browser LLM Inference Engine.**
 
 
-[Documentation](https://webllm.mlc.ai/docs/) | [Blogpost](https://blog.mlc.ai/2024/06/13/webllm-a-high-performance-in-browser-llm-inference-engine) | [Examples](examples) 
+[Documentation](https://webllm.mlc.ai/docs/) | [Blogpost](https://blog.mlc.ai/2024/06/13/webllm-a-high-performance-in-browser-llm-inference-engine) | [Paper](https://arxiv.org/abs/2412.15803) | [Examples](examples)
 
 </div>
 
@@ -455,6 +455,21 @@ This project is initiated by members from CMU Catalyst, UW SAMPL, SJTU, OctoML,
 
 This project is only possible thanks to the shoulders open-source ecosystems that we stand on. We want to thank the Apache TVM community and developers of the TVM Unity effort. The open-source ML community members made these models publicly available. PyTorch and Hugging Face communities make these models accessible. We would like to thank the teams behind Vicuna, SentencePiece, LLaMA, and Alpaca. We also would like to thank the WebAssembly, Emscripten, and WebGPU communities. Finally, thanks to Dawn and WebGPU developers.
 
+## Citation
+If you find this project to be useful, please cite:
+
+```
+@misc{ruan2024webllmhighperformanceinbrowserllm,
+      title={WebLLM: A High-Performance In-Browser LLM Inference Engine}, 
+      author={Charlie F. Ruan and Yucheng Qin and Xun Zhou and Ruihang Lai and Hongyi Jin and Yixin Dong and Bohan Hou and Meng-Shiun Yu and Yiyan Zhai and Sudeep Agarwal and Hangrui Cao and Siyuan Feng and Tianqi Chen},
+      year={2024},
+      eprint={2412.15803},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2412.15803}, 
+}
+```
+
 ## Contributors
 
 <a href="https://github.com/mlc-ai/web-llm/graphs/contributors">