Update README.md

README.md

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 ## Model Description
-We introduce Dragon-multiturn, a retriever specifically designed for the conversational QA scenario. It can handle conversational query which combine dialogue history with the current query. It is built on top of the [Dragon](https://huggingface.co/facebook/dragon-plus-query-encoder) retriever. The details of Dragon-multiturn can be found in [here](https://arxiv.org/abs/2401.10225). **Please note that Dragon-multiturn is a dual encoder consisting of a query encoder and a context encoder. This repository is only for the query encoder of Dragon-multiturn for getting the query embeddings, and you also need the context encoder to get context embeddings, which can be found [here](https://huggingface.co/nvidia/dragon-multiturn-context-encoder). Both query encoder and context encoder share the same tokenizer.**
+We introduce Dragon-multiturn, a retriever specifically designed for the conversational QA scenario. It can handle conversational query which combine dialogue history with the current query. It is built on top of the [Dragon](https://huggingface.co/facebook/dragon-plus-query-encoder) retriever. The details of Dragon-multiturn can be found in [here](https://arxiv.org/pdf/2401.10225v3). **Please note that Dragon-multiturn is a dual encoder consisting of a query encoder and a context encoder. This repository is only for the query encoder of Dragon-multiturn for getting the query embeddings, and you also need the context encoder to get context embeddings, which can be found [here](https://huggingface.co/nvidia/dragon-multiturn-context-encoder). Both query encoder and context encoder share the same tokenizer.**
 
 
 ## Other Resources
-[Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)   [Llama3-ChatQA-1.5-70B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B)   [Evaluation Data](https://huggingface.co/datasets/nvidia/ChatRAG-Bench)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data)   [Website](https://chatqa-project.github.io/)   [Paper](https://arxiv.org/abs/2401.10225)
+[Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)   [Llama3-ChatQA-1.5-70B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B)   [Evaluation Data](https://huggingface.co/datasets/nvidia/ChatRAG-Bench)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data)   [Website](https://chatqa-project.github.io/)   [Paper](https://arxiv.org/pdf/2401.10225v3)
 
 ## Benchmark Results
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@@ -141,7 +141,7 @@ Zihan Liu (zihanl@nvidia.com), Wei Ping (wping@nvidia.com)
 ## Citation
 <pre>
 @article{liu2024chatqa,
-  title={ChatQA: Building GPT-4 Level Conversational QA Models},
+  title={ChatQA: Surpassing GPT-4 on Conversational QA and RAG},
   author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},
   journal={arXiv preprint arXiv:2401.10225},
   year={2024}}