Simplified Version of Retrieval Augmented Generation (RAG)

Language models (like GPT) can be fine-tuned to handle common tasks, such as identifying sentiment or recognizing named entities, without needing extra background knowledge. However, for more complex tasks that require a lot of information, we can create systems that connect to external knowledge sources to improve the accuracy and reliability of their responses. This reduces errors, such as making up facts (often called "hallucination").

To tackle these knowledge-heavy tasks, Meta AI researchers introduced Retrieval Augmented Generation (RAG). RAG combines two things: an information retrieval system and a text generator. This approach allows the model to access and use external information without needing to retrain the entire model, making it more flexible and adaptable.

How does RAG work? When you give it input, RAG retrieves relevant information from sources like Wikipedia. The retrieved information is added to the original input and passed into the text generator, which then creates the final response. This means RAG can provide up-to-date facts without needing constant retraining, which is essential since traditional models' knowledge becomes outdated over time.

RAG has been tested and shown to work well on various tasks, like answering questions from datasets such as Natural Questions and MS-MARCO. It produces more accurate, fact-based, and diverse answers, and it also improves performance in verifying facts.

Recently, this retrieval-based approach has gained popularity, especially when combined with large models like ChatGPT, improving their ability to provide factual and consistent information.

Use Case: RAG can be applied to generate responses in complex tasks like writing user-friendly summaries of machine learning papers.

