APIGen：用于生成可验证和多样化函数调用数据集的自动化流水线

摘要

功能调用代理模型的进展需要多样化、可靠和高质量的数据集。本文介绍了APIGen，这是一个自动化数据生成管道，旨在为功能调用应用程序合成可验证的高质量数据集。我们利用APIGen，收集了21个不同类别中的3,673个可执行API，以便以可扩展和结构化的方式生成多样化的功能调用数据集。我们的数据集中的每个数据都经过三个分层阶段的验证：格式检查、实际函数执行和语义验证，确保其可靠性和正确性。我们展示了使用我们精心策划的数据集训练的模型，即使只有70亿参数，也能在伯克利功能调用基准测试中取得最先进的性能，胜过多个GPT-4模型。此外，我们的10亿参数模型表现出色，超越了GPT-3.5-Turbo和Claude-3 Haiku。我们发布了一个包含60,000个高质量条目的数据集，旨在推动功能调用代理领域的发展。该数据集可在Huggingface上获取：https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k，项目主页：https://apigen-pipeline.github.io/

English

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/

APIGen：用于生成可验证和多样化函数调用数据集的自动化流水线

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

摘要

Summary

Support

Support