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Semantic, Syntactic, Lexical: What Makes QA Augmentation Work in Limited Quantity?

Data augmentation is a common fix in domains where training data is scarce or difficult to collect, such as specialized medical or any other domain specific applications. In question answering (QA), most studies report headline accuracy while saying little about the quality of the synthetic data. Here, quality goes beyond fluent rewording: augmented items must remain faithful to the supporting evidence and preserve the original answerability. We study three augmentation families lexical, syntactic, and semantic edits generated with LLaMA 3.1 70B, and analyze how these edits affect model behavior. To mirror low-resource settings, we focus on subsets of SQuADv2 (general) and PubMedQA (biomedical, domain specific). We report Exact Match (EM)/F1 alongside quality diagnostics, yielding a fuller picture than accuracy alone. Our results show that augmentation behaves differently across domains and scales. In SQuADv2, augmented variants maintain performance on par with baselines, showing that added diversity mostly does not harm model quality, whereas in PubMedQA semantic edits bring improvements under extreme scarcity and support stronger performance as supervision grows.