GenesisFunc: 面向准确且泛化的函数调用的多智能体数据生成
GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling
AI总结：
提出GenesisFunc多智能体自动生成函数调用训练数据，通过多阶段评估保证质量，微调8B模型在域内和域外均优于同类开源模型，性能接近部分API模型。
摘要：
大型语言模型（LLM）通过函数调用（FC）扩展其能力，这依赖于高质量、多样化且覆盖广泛场景的训练数据。然而，获取和标注真实的函数调用数据具有挑战性，而现有流水线生成的合成数据通常存在API不可靠、工具可扩展性有限、多样性不足和质量控制薄弱等问题。为解决这些问题，我们提出了GenesisFunc，一个用于生成FC训练数据的自动化流水线。从广泛使用的公共基准中的可靠工具出发，我们的GenesisFunc采用多智能体框架支持对话生成系统，该系统生成涵盖多种场景的对话，同时在整个过程中保持多样性和质量。通过多阶段评估系统进一步强化数据的准确性。我们在合成数据集上微调了一个8B LLM，并通过大量实验表明，它在域内FC性能和域外泛化方面优于同等规模的开源模型，同时达到了与一些最新的基于API的模型相当的FC能力。此外，我们的方法展示了在下游工具中有效扩展的强大潜力，突显了其实际应用性。
英文摘要：
Large Language Models (LLMs) extend their capabilities through function-calling (FC), which relies on training data with high quality, diversity, and broad coverage of scenario. However, obtaining and annotating real function-calling data is challenging, while synthetic data from existing pipelines often suffers from unreliable APIs, limited tool scalability, insufficient diversity, and weak quality control. To address these, we present GenesisFunc, an automated pipeline for generating FC training data. Starting from reliable tools in widely used public benchmarks, our GenesisFunc employs a multi-agent framework to support a dialogue generation system that produces conversations spanning diverse scenarios, while maintaining both diversity and quality throughout the process. The accuracy of the data is further reinforced through a multi-stage evaluation system. We fine-tune an 8B LLM on the synthetic dataset and show through extensive experiments that it outperforms similarly sized open-source models in in-domain FC performance and out-of-domain generalization, while reaching FC capabilities comparable to some of the latest API-based models. In addition, our method demonstrates strong potential to scale effectively across downstream tools, underscoring its real-world applicability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
