What is Retrieval-Augmented Fine-Tuning (RAFT)?
Retrieval-augmented fine tuning (RAF) combines retrieval augmented generation (RAG) and fine-tuning to enhance large language models' (LLM) performance in domain-specific tasks by training models to effectively use external documents for generating answers.
MAIN POINTS FROM TRANSCRIPT
- RAF merges RAG and fine-tuning to improve LLM performance in specialized domains.
- RAG provides context during inference by retrieving relevant documents from a vector database.
- Fine-tuning incorporates domain-specific knowledge during training using a labeled dataset.
- RAF trains models to effectively utilize RAG, enhancing their ability to generate accurate answers.
TAKEAWAYS
- RAF is a hybrid approach that combines the strengths of RAG and fine-tuning.
- It enhances model performance by teaching them to retrieve and use external documents.
- RAF requires training data consisting of queries, documents, and answers.
- This method aims to improve LLMs' ability to generate domain-specific responses.