Retrieval Augmented Lm

Retrieval-Augmented Language Models (RALMs) enhance large language models (LLMs) by incorporating external knowledge retrieved from databases or knowledge bases, improving their performance on complex tasks requiring factual accuracy and reasoning. Current research focuses on improving retrieval efficiency and effectiveness through techniques like tree-based search algorithms, self-supervised learning for better document summarization and selection, and the development of novel compression methods to reduce computational costs. This area is significant because it addresses LLMs' limitations in accessing and processing large amounts of information, leading to more accurate and reliable applications in diverse fields such as question answering, text-to-SQL, and biomedical knowledge retrieval.