Summary of Apigen: Automated Pipeline For Generating Verifiable and Diverse Function-calling Datasets, by Zuxin Liu et al.
APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets
by Zuxin Liu, Thai Hoang, Jianguo Zhang, Ming Zhu, Tian Lan, Shirley Kokane, Juntao Tan, Weiran Yao, Zhiwei Liu, Yihao Feng, Rithesh Murthy, Liangwei Yang, Silvio Savarese, Juan Carlos Niebles, Huan Wang, Shelby Heinecke, Caiming Xiong
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
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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 The proposed APIGen pipeline is a game-changer for function-calling agent models, as it enables the creation of diverse, reliable, and high-quality datasets. By leveraging this pipeline, researchers can collect a vast array of executable APIs across various categories, generating scalable and structured datasets. Each data point in these datasets undergoes rigorous verification through three hierarchical stages: format checking, actual function executions, and semantic verification. This ensures the reliability and correctness of the generated datasets. The authors demonstrate that models trained with these curated datasets can achieve state-of-the-art performance on the Berkeley Function-Calling Benchmark, outperforming multiple GPT-4 models. Moreover, they show that a 1B model can achieve exceptional performance, surpassing GPT-3.5-Turbo and Claude-3 Haiku. The researchers release a dataset containing 60,000 high-quality entries, aiming to advance the field of function-calling agent domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary APIGen is a new way to create datasets for function-calling applications. It helps make sure these datasets are reliable and correct. By using this pipeline, developers can collect lots of APIs from different categories and generate large datasets that are easy to work with. Each piece of data in the dataset goes through several checks to make sure it’s good quality. The team shows that models trained with their dataset can do better than other models on a benchmark test. They also share a big dataset containing 60,000 entries so others can use it. |
Keywords
» Artificial intelligence » Claude » Gpt
