Summary of Api Pack: a Massive Multi-programming Language Dataset For Api Call Generation, by Zhen Guo et al.
API Pack: A Massive Multi-Programming Language Dataset for API Call Generation
by Zhen Guo, Adriana Meza Soria, Wei Sun, Yikang Shen, Rameswar Panda
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces API Pack, a massive multi-programming language dataset containing over one million instruction-API calls. The authors aim to improve the API call generation capabilities of large language models by fine-tuning them on this dataset. They demonstrate three key findings: first, open-source models can outperform GPT-3.5 and GPT-4 in generating code for new API calls when fine-tuned on API Pack; second, combining a large dataset from one language with smaller datasets from others improves API generation accuracy across multiple languages; third, increasing the fine-tuning data enhances generalization to new APIs. The authors open-source the API Pack dataset, trained model, and code to support further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary API Pack is a big collection of instructions that help computers talk to each other better. This helps machines learn how to write new codes for different applications. The researchers tested their ideas by training special computer programs on this data. They found that these programs got better at writing new codes when they were trained on API Pack. This is important because it means we can use these programs to make computers do more things, like helping us with tasks or answering questions. |
Keywords
* Artificial intelligence * Fine tuning * Generalization * Gpt
