Retrieval Augmented Generative

Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge bases to improve accuracy and address limitations like hallucinations. Current research focuses on improving RAG's performance in various tasks, such as question answering and scientific literature analysis, while also addressing vulnerabilities to manipulation through adversarial attacks on the retrieval component. This approach holds significant promise for advancing applications requiring reliable and verifiable information access, particularly in scientific research and information retrieval, but also necessitates robust security measures to mitigate potential biases and malicious attacks.