An Investigation of the (In)effectiveness of Counterfactually Augmented Data
This addresses a critical problem for NLP researchers and practitioners seeking robust models, but it is incremental as it builds on prior CAD work to explain mixed results.
The paper investigates why counterfactually-augmented data (CAD) often fails to improve out-of-distribution generalization in language models, showing that CAD can hinder learning of robust features and worsen spurious correlations, with results on two crowdsourced datasets indicating limited effectiveness due to lack of perturbation diversity.
While pretrained language models achieve excellent performance on natural language understanding benchmarks, they tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data. Recent work has explored using counterfactually-augmented data (CAD) -- data generated by minimally perturbing examples to flip the ground-truth label -- to identify robust features that are invariant under distribution shift. However, empirical results using CAD for OOD generalization have been mixed. To explain this discrepancy, we draw insights from a linear Gaussian model and demonstrate the pitfalls of CAD. Specifically, we show that (a) while CAD is effective at identifying robust features, it may prevent the model from learning unperturbed robust features; and (b) CAD may exacerbate existing spurious correlations in the data. On two crowdsourced CAD datasets, our results show that the lack of perturbation diversity limits their effectiveness on OOD generalization, calling for innovative crowdsourcing procedures to elicit diverse perturbation of examples.