mlsanityData to 7 Oct 2026

Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

arXiv 1909.12434 ↗First posted 26 Sep 2019cs.CL cs.AI cs.LGcited by 428

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Cited by 22 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Despite alarm over the reliance of machine learning systems on so-called spurious patterns, the term lacks coherent meaning in standard statistical frameworks. However, the language of causality offers clarity: spurious associations are due to confounding (e.g., a common cause), but not direct or indirect causal effects. In this paper, we focus on natural language processing, introducing methods and resources for training models less sensitive to spurious patterns. Given documents and their initial labels, we task humans with revising each document so that it (i) accords with a counterfactual target label; (ii) retains internal coherence; and (iii) avoids unnecessary changes. Interestingly, on sentiment analysis and natural language inference tasks, classifiers trained on original data fail on their counterfactually-revised counterparts and vice versa. Classifiers trained on combined datasets perform remarkably well, just shy of those specialized to either domain. While classifiers trained on either original or manipulated data alone are sensitive to spurious features (e.g., mentions of genre), models trained on the combined data are less sensitive to this signal. Both datasets are publicly available.

Key facts. Cited by 428 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2019.

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Citing papers per year: 2020: 1; 2021: 85; 2022: 89; 2023: 94; 2024: 51; 2025: 41; 2026: 67.

428 papers.

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  11. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 257
  12. 2023Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Taskscited by 243
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  14. 2021Measure and Improve Robustness in NLP Models: A Surveycited by 107
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  24. 2021Types of Out-of-Distribution Texts and How to Detect Themcited by 80
  25. 2021Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Testscited by 77
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  28. 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 72
  29. 2022GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspectivecited by 72
  30. 2023Discover and Cure: Concept-aware Mitigation of Spurious Correlationcited by 71
  31. 2022A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Modelscited by 68
  32. 2021Tailor: Generating and Perturbing Text with Semantic Controlscited by 67
  33. 2021"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classificationcited by 66
  34. 2021Editing a classifier by rewriting its prediction rulescited by 64
  35. 2024A Multimodal Automated Interpretability Agentcited by 62
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  37. 2021Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Predictioncited by 58
  38. 2021Mandoline: Model Evaluation under Distribution Shiftcited by 55

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