APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets

TL;DR

APIGen is an automated pipeline that synthesizes high-quality, verifiable function-calling datasets from a large collection of APIs, significantly improving model performance in function-calling tasks.

Contribution

The paper introduces APIGen, a scalable and structured data generation pipeline that creates diverse, verified datasets for function-calling models, enhancing their reliability and performance.

Findings

Models trained on APIGen datasets outperform GPT-4 on benchmarks.

A 7B parameter model achieves state-of-the-art results.

The dataset contains 60,000 high-quality entries.

Abstract

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/