Update README.md

README.md

@@ -22,6 +22,7 @@ Here is a Ext2Gen-8B-R2 model that effectively mitigates hallucinations caused b
 
 See the details in our paper [Link](https://arxiv.org/pdf/2503.04789)
 
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 ### What is Ext2Gen-8B-R2?
 Ext2Gen-8B-R2 is built upon Llama3.2-8B-Instruct, incorporating preference-aligned fine-tuning through pairwise feedback learning. 
 
@@ -30,13 +31,15 @@ This training strategy enables the model to:
 - Filter out irrelevant or misleading information, reducing hallucinations.
 - Align generation with human preferences by optimizing for faithfulness, completeness, and conciseness.
 
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 ### Why does Ext2Gen-8B-R2 outperform standard RAG models?
 Standard RAG models often struggle due to:
 - Uncertain Placement – Relevant information may appear in unpredictable locations within retrieved chunks, making it difficult for LLMs to utilize it effectively.
 - 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.
 
-### Need a More Faster Inference?
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+### Need a Faster Inference?
 Our Ext2Gen model writes the sentences related to the query first before generating the answer. So, it needs more latency before getting the answer. 
 
 If you don't want to see the extracted sentences but want to directly see the answer with low latency, use its variant we call Gen-8B-R2.
@@ -46,7 +49,6 @@ Link: https://huggingface.co/DISLab/Gen-8B-R2
 This model skips the sentence extraction phase but remains its high robustness comparable to Ext2Gen-8B-R2.
 
 
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 ### Prompt
 
 - query: the query to answer