Diving Deep into RAG: How Retrieval Augmentation Transforms Language Models

Retrieval Augmented Generation is a technique in natural language processing that seamlessly blends the strengths of retrieval-based and generative-based approaches. Unlike traditional generative models, RAG incorporates a preliminary retrieval step to enhance the contextual understanding of the input prompt. This involves leveraging methods such as sparse Retrieval, dense Retrieval, or a hybrid combination of both.

Sparse retrieval classical information retrieval techniques, such as TF-IDF or BM25, to identify specific information from a large dataset. On the other hand, dense retrieval utilizes pre-trained neural network embedding like BERT to capture semantic similarities, allowing for a more excellent understanding of context.

Integrating these retrieval methods into a generative framework enables RAG to overcome limitations associated with pure generative models, such as hallucinations and factual inaccuracies. By feeding relevant retrieved information to the generative model, Retrieval Augmented Generation generates responses.

RAG finds applications in many areas, including question answering, text summarization, and dialogue systems, providing a robust solution that combines retrieval’s precision with a generation’s creativity. This holistic approach improves the accuracy of generated content and encourages more coherent and contextually relevant language understanding in natural language processing tasks.

To get more knowledge on how Retrieval Augmented Generation transforms language models, you can watch our webinar.