APIGen: Automated Pipeline for Generating Verifiable and Diverse
  Function-Calling Datasets
Authors: name: Zuxin Liu, name: Thai Hoang, name: Jianguo Zhang, name: Ming Zhu, name: Tian Lan, name: Shirley Kokane, name: Juntao Tan, name: Weiran Yao, name: Zhiwei Liu, name: Yihao Feng, name: Rithesh Murthy, name: Liangwei Yang, name: Silvio Savarese, name: Juan Carlos Niebles, name: Huan Wang, name: Shelby Heinecke, name: Caiming Xiong

The advancement of function-calling agent models requires diverse, reliable,
and high-quality datasets. This paper presents APIGen, an automated data
generation pipeline designed to synthesize verifiable high-quality datasets for
function-calling applications. We leverage APIGen and collect 3,673 executable
APIs across 21 different categories to generate diverse function-calling
datasets in a scalable and structured manner. Each data in our dataset is
verified through three hierarchical stages: format checking, actual function
executions, and semantic verification, ensuring its reliability and
correctness. We demonstrate that models trained with our curated datasets, even
with only 7B parameters, can achieve state-of-the-art performance on the
Berkeley Function-Calling Benchmark, outperforming multiple GPT-4 models.
Moreover, our 1B model achieves exceptional performance, surpassing
GPT-3.5-Turbo and Claude-3 Haiku. We release a dataset containing 60,000
high-quality entries, aiming to advance the field of function-calling agent
domains. The dataset is available on Huggingface:
https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k and the
project homepage: https://apigen-pipeline.github.io/