  Textual data augmentation (DA) is a prolific field of study where novel
techniques to create artificial data are regularly proposed, and that has
demonstrated great efficiency on small data settings, at least for text
classification tasks. In this paper, we challenge those results, showing that
classical data augmentation is simply a way of performing better fine-tuning,
and that spending more time fine-tuning before applying data augmentation
negates its effect. This is a significant contribution as it answers several
questions that were left open in recent years, namely~: which DA technique
performs best (all of them as long as they generate data close enough to the
training set as to not impair training) and why did DA show positive results
(facilitates training of network). We furthermore show that zero and few-shot
data generation via conversational agents such as ChatGPT or LLama2 can
increase performances, concluding that this form of data augmentation does
still work, even if classical methods do not.
