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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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pipeline_tag: text-generation |
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tags: |
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- rag |
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--- |
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<div align="center"> |
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<b style="font-size: 40px;">Ext2Gen-8B-R2</b> |
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</div> |
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Note: We are still working on this. |
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Are you looking for a more robust and reliable generation model for RAG system? |
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Here is a Ext2Gen-8B-R2 model that effectively mitigates hallucinations caused by retrieval noise and information overload. |
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See the details in our paper [Link](https://arxiv.org/pdf/2503.04789) |
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### What is Ext2Gen-8B-R2? |
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Ext2Gen-8B-R2 is built upon Llama3.2-8B-Instruct, incorporating preference-aligned fine-tuning through pairwise feedback learning. |
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This training strategy enables the model to: |
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- Extract highly relevant sentences from retrieved chunks before generating an answer. |
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- Filter out irrelevant or misleading information, reducing hallucinations. |
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- 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? |
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Standard RAG models often struggle due to: |
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- Uncertain Placement – Relevant information may appear in unpredictable locations within retrieved chunks, making it difficult for LLMs to utilize it effectively. |
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- Information Overload – The presence of irrelevant chunks can distract the model, leading to errors or hallucinations. |
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- Lack of Alignment – Most generation models are not explicitly trained to prioritize relevant content over noise. |
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### Prompt |
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TBD |
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### Performance Benchmark |
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Our evaluations demonstrate that Ext2Gen-8B-R2 significantly enhances robustness in RAG systems: |
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* We conduct a QA task using RAG Systems on NQ, MS-MARCO, HotpotQA datasets. |
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* The difference is the generation backbone: Llama3.1-8B-Instruct vs. Ext2Gen-8B-R2 |
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See the results in the Figure below: |
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