Techniques leveraging trained models may be more costly to implement but introduce more data variation, leading to better performance boosts. 

University of Michigan - Ann Arbor

The trade-off is often based on development cost and data variation.

Trade-off of DA Tricks in NLP

https://arxiv-download.xixiaoyao.cn/pdf/2105.03075.pdf

A Survey of Data Augmentation Approaches for NLP

Trade-off of Leveraging Trained Models DA Techniques

Rule-based techniques are easy-to-implement but usually offer incremental performance improvements.

Trade-off of Rule-based DA Techniques

In natural language processing (NLP), model-based data augmentation techniques customized for downstream tasks can have strong positive effects on performance, but they are often difficult to develop and utilize.

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