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.
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.
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.
Recommended Prompt
- query: the query to answer
- chunk_list: the list of retrieved chunks, e.g., ["chunk 1", "chunk 2", "chunk 3"]
def prepare_sample_text(prompt):
row_json = [{"role": "user", "content": prompt}]
return tokenizer.apply_chat_template(row_json, tokenize=False)
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 = '
'.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()
prompt = format_prompt_template(query, noisy_chunks)
prompt = prepare_sample_text(prompt)
Note that this prompt outputs both extracted relevant sentences and the answer to the query.
The output follows a consistent format as seen in an example below.
Extracted Sentences:
- The estimated number of deaths is 150-300,000, mainly Jews.
Answer: The estimated number of deaths at Chelmno is 150-300,000, mainly Jews.
The number of extracted sentences vary depending on the QA.
Recommended Generation Parameters
max_new_tokens=1024, # or 2048
do_sample=True,
temperature=0.8,
top_p=0.9,
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:
