Tool Use

Tool use in artificial intelligence focuses on enabling large language models (LLMs) to interact with external tools and data sources to enhance their capabilities and overcome limitations like error propagation and hallucination. Current research emphasizes improving the efficiency and accuracy of tool selection and invocation, often employing techniques like chain-of-thought reasoning and fine-tuning methods tailored to specific tool types (e.g., those handling tabular data or code). This research is significant because it addresses critical challenges in LLM development, leading to more robust and versatile AI systems with applications across diverse fields, including finance and software development.