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Retrieval-Augmented Generation (RAG) Overview
High-Level Overview:
RAG, or Retrieval-Augmented Generation, is a model that combines the benefits of large pre-trained language models with the capability to retrieve and use external knowledge. It effectively fuses the generation capabilities of models like BART with retrieval mechanisms akin to DPR (Dense Passage Retrieval). The primary goal of RAG is to enhance generative models with the ability to pull in real-world, factual information from external sources, making the generated outputs more accurate, diverse, and factual.
Detailed Explanation:
Components of RAG:
Workflow:
Training:
Benefits:
Key Results:
Challenges and Considerations:
Retrieval of Irrelevant Documents:
Retrieval Collapse:
Latency and Computational Overhead:
Diverse and Unpredictable Outputs:
In essence, RAG represents a significant step forward in the quest to build AI models that can generate outputs grounded in real-world facts and knowledge.
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