تقرير
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
العنوان: | ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs |
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المؤلفون: | Qin, Yujia, Liang, Shihao, Ye, Yining, Zhu, Kunlun, Yan, Lan, Lu, Yaxi, Lin, Yankai, Cong, Xin, Tang, Xiangru, Qian, Bill, Zhao, Sihan, Hong, Lauren, Tian, Runchu, Xie, Ruobing, Zhou, Jie, Gerstein, Mark, Li, Dahai, Liu, Zhiyuan, Sun, Maosong |
سنة النشر: | 2023 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning |
الوصف: | 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. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2307.16789 |
رقم الأكسشن: | edsarx.2307.16789 |
قاعدة البيانات: | arXiv |
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