mlsanityData to 14 Oct 2026

On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference

arXiv 1907.04389 ↗First posted 9 Jul 2019cs.CLcited by 29

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Abstract (arXiv)

Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jul 2019.

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Citing papers per year: 2021: 7; 2022: 10; 2023: 6; 2024: 1; 2025: 3; 2026: 2.

29 papers.

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  1. 2022Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasetscited by 54
  2. 2021Variational Information Bottleneck for Effective Low-Resource Fine-Tuningcited by 51
  3. 2022Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusterscited by 44
  4. 2021Supervising Model Attention with Human Explanations for Robust Natural Language Inferencecited by 29
  5. 2025Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcutscited by 29
  6. 2021Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognitioncited by 19
  7. 2022Saving Dense Retriever from Shortcut Dependency in Conversational Searchcited by 19
  8. 2022MENLI: Robust Evaluation Metrics from Natural Language Inferencecited by 18
  9. 2022Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptivelycited by 18
  10. 2021Uncertainty Calibration for Ensemble-Based Debiasing Methodscited by 16
  11. 2022Feature-Level Debiased Natural Language Understandingcited by 10
  12. 2023Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentationcited by 7
17 more
  1. 2023Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyondcited by 7
  2. 2022Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learningcited by 5
  3. 2021Learning to Learn to be Right for the Right Reasonscited by 4
  4. 2022Less is Better: Recovering Intended-Feature Subspace to Robustify NLU Modelscited by 4
  5. 2023Towards Fine-Grained Information: Identifying the Type and Location of Translation Errorscited by 4
  6. 2023A Simple yet Effective Self-Debiasing Framework for Transformer Modelscited by 4
  7. 2025Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selectioncited by 4
  8. 2021A Generative Approach for Mitigating Structural Biases in Natural Language Inferencecited by 3
  9. 2022Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Modelscited by 3
  10. 2024Explanation based Bias Decoupling Regularization for Natural Language Inferencecited by 3
  11. 2023SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biasescited by 2
  12. 2023IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU modelscited by 2
  13. 2025Mitigating Shortcut Learning with InterpoLated Learningcited by 2
  14. 2021Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfercited by 1
  15. 2022KL Regularized Normalization Framework for Low Resource Taskscited by 0
  16. 2026SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understandingcited by 0
  17. 2026Judging by the Cover: Cleaning LLM Truthfulness Benchmarks to Avoid Surface-Level Feature Leakagecited by 0