ToolACE: Winning the Points of LLM Function Calling
Weiwen Liu Xu Huang Xingshan Zeng xinlong hao Shuai Yu Dexun Li Shuai Wang Weinan Gan Zhengying Liu Yuanqing Yu Zezhong WANG Yuxian Wang Wu Ning Yutai Hou Bin Wang Chuhan Wu Wang Xinzhi Yong Liu Yasheng Wang Duyu Tang et al. (7 additional authors not shown)
Function calling significantly extends the application boundary of large language models (LLMs), where high-quality and diverse training data is critical for unlocking this capability. However, collecting and annotating real function-calling data is challenging, while synthetic data from existing pipelines often lack coverage and accuracy. In this paper, we present ToolACE, an automatic agentic pipeline designed to generate accurate, complex, and diverse tool-learning data, specifically tailored to the capabilities of LLMs. ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs. Dialogs are further generated through the interplay among multiple agents, under the guidance of a complexity evaluator. To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks. We demonstrate that models trained on our synthesized data---even with only 8B parameters---achieve state-of-the-art performance, comparable to the latest GPT-4 models. Our model and a subset of the data are publicly available at https://huggingface.co/Team-ACE.
Tool leaning, Function calling, Large language models
Improving the function calling capability of large language models with data accuracy, diversity, and complexity.