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

arXiv: https://arxiv.org/abs/1907.04389 · HTML: https://mlsanity.com/paper/1907.04389/

## Key facts

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

## Built on by (most cited first, 29 of 29)

- [Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets](https://mlsanity.com/paper/2203.12942/) (2022)
- [Variational Information Bottleneck for Effective Low-Resource Fine-Tuning](https://mlsanity.com/paper/2106.05469/) (2021)
- [Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters](https://mlsanity.com/paper/2204.07447/) (2022)
- [Supervising Model Attention with Human Explanations for Robust Natural Language Inference](https://mlsanity.com/paper/2104.08142/) (2021)
- [Benchmark Designers Should "Train on the Test Set" to Expose Exploitable Non-Visual Shortcuts](https://mlsanity.com/paper/2511.04655/) (2025)
- [Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition](https://mlsanity.com/paper/2107.11610/) (2021)
- [Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://mlsanity.com/paper/2202.07280/) (2022)
- [MENLI: Robust Evaluation Metrics from Natural Language Inference](https://mlsanity.com/paper/2208.07316/) (2022)
- [Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively](https://mlsanity.com/paper/2211.01642/) (2022)
- [Uncertainty Calibration for Ensemble-Based Debiasing Methods](https://mlsanity.com/paper/2111.04104/) (2021)
- [Feature-Level Debiased Natural Language Understanding](https://mlsanity.com/paper/2212.05421/) (2022)
- [Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation](https://mlsanity.com/paper/2305.13067/) (2023)
- [Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond](https://mlsanity.com/paper/2310.14670/) (2023)
- [Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning](https://mlsanity.com/paper/2212.10392/) (2022)
- [Learning to Learn to be Right for the Right Reasons](https://mlsanity.com/paper/2104.11514/) (2021)
- [Less is Better: Recovering Intended-Feature Subspace to Robustify NLU Models](https://mlsanity.com/paper/2209.07879/) (2022)
- [Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors](https://mlsanity.com/paper/2302.08975/) (2023)
- [A Simple yet Effective Self-Debiasing Framework for Transformer Models](https://mlsanity.com/paper/2306.01907/) (2023)
- [Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection](https://mlsanity.com/paper/2505.20209/) (2025)
- [A Generative Approach for Mitigating Structural Biases in Natural Language Inference](https://mlsanity.com/paper/2108.14006/) (2021)
- [Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models](https://mlsanity.com/paper/2212.08399/) (2022)
- [Explanation based Bias Decoupling Regularization for Natural Language Inference](https://mlsanity.com/paper/2404.13390/) (2024)
- [SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases](https://mlsanity.com/paper/2302.14413/) (2023)
- [IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models](https://mlsanity.com/paper/2311.00292/) (2023)
- [Mitigating Shortcut Learning with InterpoLated Learning](https://mlsanity.com/paper/2507.05527/) (2025)
- [Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer](https://mlsanity.com/paper/2109.03819/) (2021)
- [KL Regularized Normalization Framework for Low Resource Tasks](https://mlsanity.com/paper/2212.11275/) (2022)
- [SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understanding](https://mlsanity.com/paper/2606.03284/) (2026)
- [Judging by the Cover: Cleaning LLM Truthfulness Benchmarks to Avoid Surface-Level Feature Leakage](https://mlsanity.com/paper/2609.13003/) (2026)

## 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.