Tool Invocation

Tool invocation research focuses on enabling large language models (LLMs) to effectively utilize external tools to solve complex tasks beyond their inherent capabilities. Current efforts concentrate on improving tool retrieval and selection, often employing retrieval-augmented generation (RAG) frameworks and novel architectures like "Summary to Action" pipelines that guide LLMs through iterative tool usage. These advancements aim to mitigate LLMs' limitations, such as hallucinations and inconsistent performance, leading to more reliable and robust AI agents capable of handling real-world problems, particularly in knowledge-intensive domains like medicine.