to download project base paper of large language models.


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

toolllm-facilitating-large language models-to-master-16000-real-world-apis, final year projects.
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