---
license: apache-2.0
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
tags:
- rag
---


<div align="center">
  <b style="font-size: 40px;">Ext2Gen-8B-R2</b>
</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.
- Align generation with human preferences by optimizing for faithfulness, completeness, and conciseness.

### 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.

### Prompt

TBD


### Performance Benchmark
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)