ToolACE: Winning the Points of LLM Function Calling
This addresses the data bottleneck for LLM function calling applications, offering a scalable solution for developers and researchers, though it appears incremental as an improved data generation method.
The paper tackles the challenge of generating high-quality training data for LLM function calling by introducing ToolACE, an automatic agentic pipeline that creates accurate, complex, and diverse tool-learning data. The result shows that models trained on this synthesized data achieve state-of-the-art performance on the Berkeley Function-Calling Leaderboard, rivaling GPT-4 models with only 8B parameters.
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.