  Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed
inputs during training -- helps reduce model reliance on spurious correlations
and improves generalization to out-of-distribution (OOD) data. Prior work on
generating counterfactuals only considered restricted classes of perturbations,
limiting their effectiveness. We present COunterfactual Generation via
Retrieval and Editing (CORE), a retrieval-augmented generation framework for
creating diverse counterfactual perturbations for CDA. For each training
example, CORE first performs a dense retrieval over a task-related unlabeled
text corpus using a learned bi-encoder and extracts relevant counterfactual
excerpts. CORE then incorporates these into prompts to a large language model
with few-shot learning capabilities, for counterfactual editing. Conditioning
language model edits on naturally occurring data results in diverse
perturbations. Experiments on natural language inference and sentiment analysis
benchmarks show that CORE counterfactuals are more effective at improving
generalization to OOD data compared to other DA approaches. We also show that
the CORE retrieval framework can be used to encourage diversity in manually
authored perturbations
