Retrieval Augmented Generation

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge sources to improve accuracy and address limitations like hallucinations. Current research focuses on optimizing retrieval strategies (e.g., using hierarchical graphs, attention mechanisms, or determinantal point processes for diverse and relevant information selection), improving the integration of retrieved information with LLM generation (e.g., through various prompting techniques and model adaptation methods), and mitigating biases and ensuring fairness in RAG systems. The impact of RAG is significant, improving performance on various tasks like question answering and enabling more reliable and contextually aware applications across diverse domains, including healthcare and scientific research.