Update README.md

README.md

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 </div>
 
 Are you looking for a more robust and reliable generation model for RAG system?
+
 Here is a Ext2Gen-8B-R2 model that effectively mitigates hallucinations caused by retrieval noise and information overload.
 
+See the details in our paper [Link](https://arxiv.org/pdf/2503.04789)
+
 ### What is Ext2Gen-8B-R2?
 Ext2Gen-8B-R2 is built upon Llama3.2-8B-Instruct, incorporating preference-aligned fine-tuning through pairwise feedback learning. 
+
 This training strategy enables the model to:
 - Extract highly relevant sentences from retrieved chunks before generating an answer.
 - Filter out irrelevant or misleading information, reducing hallucinations.
@@ -30,8 +34,19 @@ Standard RAG models often struggle due to:
 - Information Overload – The presence of irrelevant chunks can distract the model, leading to errors or hallucinations.
 - Lack of Alignment – Most generation models are not explicitly trained to prioritize relevant content over noise.
 
+### Prompt
+
+TBD
+
+
 ### Performance Benchmark
-Our extensive evaluations demonstrate that Ext2Gen-8B-R2 significantly enhances robustness in RAG systems:
+Our evaluations demonstrate that Ext2Gen-8B-R2 significantly enhances robustness in RAG systems:
+* We conduct a QA task using RAG Systems on NQ, MS-MARCO, HotpotQA datasets.
+* The difference is the generation backbone: Llama3.1-8B-Instruct vs. Ext2Gen-8B-R2
+
+See the results in the Figure below:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63c9da8d5fdc575773c84816/4mbreGv3QNxKOY8HzCLxx.png)