API Pack: A Massive Multi-Programming Language Dataset for API Call Generation

Zhen Guo, Adriana Meza Soria, Wei Sun, Yikang Shen, Rameswar Panda

Keywords: Instruction Datasets, Tool Datasets, API Datasets, Code Datasets, Synthetic Data Generation

TL;DR: API Pack, a 1M multi-programming language dataset to improve API call generation capabilities of LLMs.

Abstract: We introduce API Pack, a massive multi-programming language dataset containing over one million instruction-API calls for improving the API call generation capabilities of large language models. Our evaluation highlights three key findings: First, fine-tuning on API Pack enables open-source models to outperform GPT-3.5 and GPT-4 in generating code for entirely new API calls. We show this by fine-tuning CodeLlama-13B on 20,000 Python instances from API Pack. Second, fine-tuning on a large dataset in one language, combined with smaller datasets from others, improves API generation accuracy across multiple languages. Third, we confirm the benefits of larger datasets for API generalization, as increasing fine-tuning data to one million instances enhances generalization to new APIs. To support further research, we open-source the API Pack dataset, trained model, and code at https://github.com/zguo0525/API-Pack.

Primary Area: datasets and benchmarks

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Submission Number: 7834