RAG Overview Slides

Summary: RAG (Retrieval Augmented Generation) is an advanced NLP model developed by Facebook AI Research, combining retrieval and generation techniques to enhance text generation by accessing and incorporating information from large knowledge sources. It offers advantages such as improved contextual understanding, efficient information retrieval, and more accurate response generation, with key components including the retriever and generator, and connections to related NLP models and knowledge graphs.
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RAG (Retrieval Augmented Generation)

RAG, short for Retrieval Augmented Generation, is a cutting-edge natural language processing model that combines the capabilities of retrieval-based and generation-based models to enhance the quality of text generation tasks. Developed by the team at Facebook AI Research (FAIR), RAG has gained significant attention in the field of AI and NLP due to its ability to retrieve relevant information from large-scale knowledge sources and incorporate it into the generation process.

Overview

RAG leverages a retriever component to efficiently retrieve relevant passages from a knowledge source, such as a large text corpus or a knowledge graph. These retrieved passages are then used by the generator component to produce coherent and informative text outputs. By combining retrieval and generation techniques, RAG can generate more accurate and contextually relevant responses compared to traditional language models.

Advantages of RAG

RAG offers several advantages over conventional language models, including:

1. Enhanced Contextual Understanding	RAG can access external knowledge sources to improve the contextual understanding of generated text.
2. Improved Information Retrieval	The retriever component in RAG enables efficient retrieval of relevant information from large knowledge bases.
3. Better Response Generation	RAG's generation component can utilize retrieved passages to generate more accurate and informative responses.

Terminology

Some key terminology associated with RAG includes:

Retriever: The component responsible for retrieving relevant passages from knowledge sources.
Generator: The component that generates text based on the retrieved information.
Knowledge Source: The repository of information from which RAG retrieves relevant passages.

Building Blocks of RAG

The building blocks of RAG include:

Retriever Component: Responsible for retrieving relevant passages.
Generator Component: Utilizes retrieved information to generate text.
Knowledge Source: The external repository of information.

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RAG Overview Slides