用自进化框架生成高质量函数调用数据，让小模型也能媲美GPT-4。
ToolACE: Winning the Points of LLM Function Calling
- 通过多智能体协作与自进化机制生成复杂函数数据。
- 26,507个API构成全面工具库，8B参数模型达领先水平。
- 双层验证保障数据准确，适合训练具身智能与工具调用系统。
函数调用显著拓展了大语言模型的应用边界，而高质量、多样化的训练数据是实现该能力的关键。然而，真实函数调用数据难以收集和标注，现有合成数据管道生成的数据往往覆盖不足且准确性差。本文提出 ToolACE，一个自动化的智能体式数据生成流水线，能够生成准确、复杂且多样化的工具学习数据。ToolACE 采用新颖的自进化合成过程，构建包含 26,507 个多样化 API 的完整接口池。通过多个智能体在形式化思维流程引导下的交互，生成对话数据。为确保数据准确性，我们设计了基于规则与模型的双层验证系统。实验表明，仅使用 8B 参数的模型在 Berkeley Function-Calling Leaderboard 上即达到当前最优性能，可与最新 GPT-4 模型相媲美。我们的模型及部分数据已公开于 https://huggingface.co/Team-ACE。
原文摘要 · Abstract (English)
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. However, real function-calling data is quite challenging to collect and annotate, while synthetic data generated by existing pipelines tends to lack coverage and accuracy. In this paper, we present ToolACE, an automatic agentic pipeline designed to generate accurate, complex, and diverse tool-learning data. 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, guided by a formalized thinking process. 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 on the Berkeley Function-Calling Leaderboard, rivaling the latest GPT-4 models. Our model and a subset of the data are publicly available at https://huggingface.co/Team-ACE.
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