arXiv Machine Learning August 27, 2026 By Yilong Wang, Qianli Wang, Nils Feldhus

iFlip: Iterative Feedback-driven Counterfactual Example Refinement

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iFlip is an iterative refinement method for generating counterfactual examples using large language models. It incorporates three feedback types—model confidence, feature attribution, and natural language—to guide successive edits. Experiments show iFlip outperforms five state‑of‑the‑art baselines, achieving a 57.8% higher validity rate and improving model performance through counterfactual data augmentation.

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