Augmented Generation

Augmented generation enhances large language models (LLMs) by incorporating external knowledge to improve accuracy, address hallucination, and expand their capabilities beyond the limitations of their training data. Current research focuses on improving retrieval methods, exploring various architectures like Table-Augmented Generation (TAG) and Induction-Augmented Generation (IAG), and developing techniques to efficiently manage large context windows. This field is significant because it addresses critical limitations of LLMs, leading to more reliable and robust applications in question answering, IT support, and database interaction, among other areas.