Retrieval Augmented Language Model

Retrieval-Augmented Language Models (RALMs) enhance large language models (LLMs) by incorporating external knowledge sources during inference, aiming to improve accuracy and address limitations like hallucinations and outdated information. Current research focuses on improving retrieval methods, refining the integration of retrieved information with LLM generation (e.g., through techniques like in-context learning and knowledge rewriting), and developing robust evaluation frameworks to assess RALM performance across diverse tasks. This field is significant because RALMs offer a path towards more reliable, adaptable, and trustworthy LLMs, with potential applications ranging from clinical medicine to scientific research and beyond.