Easy Tool

Research on "easy tools" focuses on augmenting large language models (LLMs) and other machine learning models with external tools to enhance their capabilities for complex tasks, particularly those requiring reasoning, information retrieval, or interaction with the real world. Current efforts concentrate on developing frameworks for efficient tool integration, including methods for tool selection, execution, and dynamic plan adjustment, often employing techniques like retrieval-based systems and cooperative multi-agent architectures. This research is significant because it addresses limitations of LLMs in handling tasks beyond their pre-trained knowledge, leading to improved performance in diverse applications such as medical coding, forecasting, and scientific literature review.