APIGen：用於生成可驗證和多樣化函數調用數據集的自動化流程

摘要

功能調用代理模型的進步需要多樣化、可靠且高質量的數據集。本文介紹了 APIGen，一個自動數據生成管道，旨在為功能調用應用程序合成可驗證的高質量數據集。我們利用 APIGen 收集了 21 個不同類別中的 3,673 個可執行 API，以便以可擴展和結構化的方式生成多樣化的功能調用數據集。我們的數據集中的每個數據都通過三個階段的層次驗證：格式檢查、實際功能執行和語義驗證，確保其可靠性和正確性。我們展示了使用我們精心策劃的數據集訓練的模型，即使只有 7B 參數，也能在伯克利功能調用基準測試中實現最先進的性能，勝過多個 GPT-4 模型。此外，我們的 1B 模型實現了出色的性能，超越了 GPT-3.5-Turbo 和 Claude-3 Haiku。我們發布了一個包含 60,000 條高質量條目的數據集，旨在推動功能調用代理領域的發展。該數據集可在 Huggingface 上找到：https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k，項目主頁：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：用於生成可驗證和多樣化函數調用數據集的自動化流程

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

摘要

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