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Despite the advancements of open-source large language models (LLMs), e.g.,LLaMA, they remain significantly limited in tool-use capabilities, i.e., usingexternal tools (APIs) to fulfill human instructions. The reason is that currentinstruction tuning largely focuses on basic language tasks but ignores thetool-use domain. This is in contrast to the excellent tool-use capabilities ofstate-of-the-art (SOTA) closed-source LLMs, e.g., ChatGPT. To bridge this gap,we introduce ToolLLM, a general tool-use framework encompassing dataconstruction, model training, and evaluation. We first present ToolBench, aninstruction-tuning dataset for tool use, which is constructed automaticallyusing ChatGPT. Specifically, the construction can be divided into three stages:(i) API collection: we collect 16,464 real-world RESTful APIs spanning 49categories from RapidAPI Hub; (ii) instruction generation: we prompt ChatGPT togenerate diverse instructions involving these APIs, covering both single-tooland multi-tool scenarios; (iii) solution path annotation: we use ChatGPT tosearch for a valid solution path (chain of API calls) for each instruction. Toenhance the reasoning capabilities of LLMs, we develop a novel depth-firstsearch-based decision tree algorithm. It enables LLMs to evaluate multiplereasoning traces and expand the search space. Moreover, to evaluate thetool-use capabilities of LLMs, we develop an automatic evaluator: ToolEval.Based on ToolBench, we fine-tune LLaMA to obtain an LLM ToolLLaMA, and equip itwith a neural API retriever to recommend appropriate APIs for each instruction.Experiments show that ToolLLaMA demonstrates a remarkable ability to executecomplex instructions and generalize to unseen APIs, and exhibits comparableperformance to ChatGPT. Our ToolLLaMA also demonstrates strong zero-shotgeneralization ability in an out-of-distribution tool-use dataset: APIBench.

ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs