Augmented Language Model

Augmented Language Models (ALMs) enhance the capabilities of large language models (LLMs) by integrating external tools and knowledge sources, aiming to improve reasoning, address limitations in factual knowledge, and enable interaction with the real world. Current research focuses on efficient methods for incorporating diverse modalities (text, images, audio, etc.), developing frameworks for tool use and reasoning, and evaluating ALMs' performance across various tasks, including question answering, translation, and even crime investigation. This rapidly evolving field holds significant promise for advancing natural language processing and enabling more robust, versatile, and reliable AI systems across numerous applications.