Retrieval Augmented Text Generation

Retrieval-augmented text generation (RAG) enhances large language models by incorporating external knowledge bases to improve accuracy and address issues like hallucinations. Current research focuses on improving efficiency (e.g., through on-device retrieval and optimized model architectures like FiD-Light), refining retrieval methods (e.g., using iterative loops or generative knowledge to improve passage ranking), and exploring multi-task learning and annotation-free training paradigms. RAG's significance lies in its ability to create more reliable and informative text generation systems, with applications ranging from personalized content creation to knowledge-intensive tasks like question answering.