Tool Retrieval

Tool retrieval focuses on efficiently selecting the most relevant external tools for large language models (LLMs) to execute complex tasks, overcoming limitations in context window size and improving task completion accuracy. Current research emphasizes developing efficient and scalable methods, including generative models that integrate tool knowledge directly into the LLM, vector space representations for tools, and iterative feedback mechanisms to refine retrieval accuracy. These advancements are crucial for building more capable and adaptable AI agents, improving the performance of LLMs across diverse applications and potentially leading to more robust and efficient AI systems.