RAFT: Adapting Language Model to Domain Specific RAG

The RAFT (Retrieval Augmentation Fine-Tuning) paper presents a method that improves retrieval augmented language models by fine-tuning them on domain-specific data. This approach allows the model to better utilize context from retrieved documents, leading to more accurate and relevant responses. RAFT is particularly useful in specialized domains where traditional document sources may not be effective. The authors demonstrate the effectiveness of RAFT through experiments on various question answering datasets, showing that it outperforms other methods, including GPT-3.5, in most cases.