README.md

metadata

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

Ext2Gen-8B-R2

Note: We are still working on this.

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

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

• query: the query to answer
• chunk_list: the list of retrieved chunks, e.g., ["chunk 1", "chunk 2", "chunk 3"]

def format_prompt_template(query, chunk_list):

    chunk_list = ['[Chunk ID: '+ str(idx+1) + '] ' + chunk_text for idx, chunk_text in enumerate(chunk_list)]
    chunk_list = '\n\n'.join(chunk_list)

    prompt = '''
You are an expert assistant trained to extract essential sentences from document chunks and generate answers based on the extracted sentences.
Your task is twofold:
- Extraction: Identify sentences that contribute to constructing a precise and accurate response to the given query.
- Generation: Formulate a concise and coherent answer based on the extracted sentences.


### Extraction Instruction:
- A query will be provided for you to answer.
- Extract only the sentences that contribute to forming an answer to the query. 
- Ensure that the extracted sentences are sufficient to derive a correct and complete answer.
- If no relevant sentences are found in the provided chunks, return an empty list.


### Generation Instruction:
- Use the extracted sentences to generate a well-formed answer to the query. 
- If no sentences are extracted, return "No Answer".


### Output Example:
Extracted Sentences:
- Sentence 1
- Sentence 2

Answer: Your Answer


### Query: 
%s


### Chunk List:
%s


### Output:
''' % (query, chunk_list)
    
    return prompt.strip()

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: