Retrieval-Augmented Generation (RAG)
Definition
Retrieval-Augmented Generation (RAG) is a technique in natural language processing where a model generates text by combining retrieved information with its own knowledge.
Explain Like I'm 5
Imagine you have a magic book that helps you write stories. You can ask the book questions, and it gives you answers that you can use to create your own story. That's kind of how Retrieval-Augmented Generation works!
Visualization
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Digging Deeper
Retrieval-Augmented Generation (RAG) combines the benefits of retrieval-based and generative approaches in natural language processing. It involves retrieving relevant information from a large database using a retriever module, which is then used by a generator module to produce text. This approach allows the model to incorporate external knowledge while generating coherent and informative responses. For example, in question-answering tasks, RAG can retrieve relevant passages from a knowledge base and use them to generate accurate answers.
Applications
- Question-Answering Systems: RAG can be used to improve the performance of question-answering systems by providing additional context from external sources.
- Chatbots: RAG can enhance chatbot responses by incorporating relevant information retrieved from databases or online sources.
- Document Summarization: RAG can aid in summarizing large documents by extracting key information using retrieval methods before generating concise summaries.
- Content Creation: RAG can assist content creators in generating articles or reports by retrieving and incorporating relevant facts or details.
- Language Translation: RAG can improve translation quality by utilizing retrieved bilingual data during the generation process.