Retrieval Augmented Generation (RAG)
May 1, 2024
3 minute read
Retrieval Augmented Generation (RAG) is a powerful technique that combines retrieval-based and generative approaches to improve the performance of natural language generation tasks. RAG has gained significant popularity in recent years due to its ability to generate more informative, coherent, and factually correct text compared to traditional generative models.
Understanding Retrieval Augmented Generation
RAG models are typically composed of two main components: a retrieval component and a generation component. The retrieval component retrieves a set of relevant documents from a large corpus based on the input query. These retrieved documents provide contextual information that is used by the generation component to generate the output text.
The generation component is typically a transformer-based language model, such as GPT-3 or T5. It takes the retrieved documents as input and generates the output text by predicting the next word in the sequence based on the context provided by the retrieved documents.
Benefits of Retrieval Augmented Generation
RAG offers several advantages over traditional generative models:
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Find a path to becoming a Retrieval Augmented Generation (RAG). Learn more at:
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Reading list
We've selected five books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Retrieval Augmented Generation (RAG).
Explores deep learning techniques for natural language processing, including retrieval-based methods. It offers a comprehensive treatment of the latest developments and is suitable for researchers and practitioners with a background in machine learning.
Covers various aspects of natural language generation, including retrieval-based approaches and their integration with generative models. It provides a broad perspective on the field and is suitable for researchers and practitioners from diverse backgrounds.
Provides a foundation in information retrieval techniques, which are fundamental to Retrieval Augmented Generation. It is particularly useful for researchers and practitioners seeking a comprehensive understanding of the underlying concepts.
This comprehensive textbook on natural language processing covers various topics, including retrieval-based methods. It is suitable for students, researchers, and practitioners seeking a broad foundation in the field.
Discusses machine translation techniques, including retrieval-based approaches. It is particularly relevant for researchers and practitioners working on cross-lingual applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/mqjiq4/retrieval
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