Retrieval Augmented Baseline

Retrieval Augmented Generation (RAG) methods enhance large language models by incorporating external knowledge sources during text generation, aiming to improve accuracy and reduce hallucinations. Current research focuses on improving the efficiency and relevance of knowledge retrieval, often employing iterative processes that synergistically combine retrieval and generation steps, and exploring different model architectures to optimize both effectiveness and storage requirements. This approach holds significant promise for advancing various natural language processing tasks, particularly those requiring factual accuracy and robust reasoning, such as question answering and long-form content generation.