GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling
For developers of LLM-based function-calling systems, this provides a scalable, automated data generation method that overcomes the limitations of existing synthetic pipelines.
GenesisFunc introduces a multi-agent pipeline for generating high-quality, diverse synthetic training data for function-calling in LLMs. Fine-tuning an 8B model on this data achieves state-of-the-art in-domain performance and out-of-domain generalization, matching some API-based models.
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.