People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection. (arXiv:2311.01270v1 [cs.CL])

NLP models are used in a variety of critical social computing tasks, such as
detecting sexist, racist, or otherwise hateful content. Therefore, it is
imperative that these models are robust to spurious features. Past work has
attempted to tackle such spurious features using training data augmentation,
including Counterfactually Augmented Data (CADs). CADs introduce minimal
changes to existing training data points and flip their labels; training on
them may reduce model dependency on spurious features. However, manually
generating CADs can be …

arxiv augmented data computing data detection features generated language llm measuring nlp nlp models people social social computing tasks work