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

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arxiv 2406.18518 v1 pith:DJQ47AEO submitted 2024-06-26 cs.CL cs.AIcs.LGcs.SE

Zuxin Liu , Thai Hoang , Jianguo Zhang , Ming Zhu , Tian Lan , Shirley Kokane , Juntao Tan , Weiran Yao
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Zhiwei Liu Yihao Feng Rithesh Murthy Liangwei Yang Silvio Savarese Juan Carlos Niebles Huan Wang Shelby Heinecke Caiming Xiong
This is my paper
classification cs.CLcs.AIcs.LGcs.SE
keywords datasetsfunction-callingapigendatasetdiversehigh-qualitymodelsagent
verification ladder T0 review T1 audit T2 compute T3 formal
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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/

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