Retrieval Augmented

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) and other machine learning models by incorporating external knowledge sources during inference, improving accuracy and addressing limitations like hallucinations and factual errors. Current research focuses on optimizing retrieval methods (e.g., using graph structures, determinantal point processes, or hierarchical representations), improving the integration of retrieved information with LLMs (e.g., through various reasoning modules and adaptive retrieval strategies), and applying RAG across diverse domains, including autonomous vehicles, robotics, and biomedical applications. This approach significantly impacts various fields by improving the reliability and efficiency of AI systems, particularly in knowledge-intensive tasks where access to and effective use of external information is crucial.