Papers with debiasing framework
Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)
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| Challenge: | Existing methods for debiasing may generate incorrect or nonsensical predictions but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. |
| Approach: | They propose a framework that identifies encoding locations of biases within language models and then applies the Fairness-Stamp (FAST) they also propose 'BiaScope' to evaluate the retention of commonsense knowledge and generalization across paraphrased social biase. |
| Outcome: | The proposed framework surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and prediction. |
Towards Stable Natural Language Understanding via Information Entropy Guided Debiasing (2023.acl-long)
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| Challenge: | Existing approaches to debiase Natural Language Understanding models use dataset biases instead of learning the intended task. |
| Approach: | They propose a debiasing framework that detects and purifies dataset biases using information entropy. |
| Outcome: | The proposed framework improves the stability of performance on out-of-distribution datasets for a set of widely adopted NLU models. |
End-to-End Self-Debiasing Framework for Robust NLU Training (2021.findings-acl)
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| Challenge: | Existing models incorporate dataset biases leading to strong performance on in-distribution test sets but poor performance on out-of-distortion (OOD) tests. |
| Approach: | They propose a debiasing framework where the shallow representations of the main model are used to derive a bias model and both models are trained simultaneously. |
| Outcome: | The proposed framework outperforms existing approaches on three well-studied NLU tasks while still delivering high in-distribution performance. |
Improving Bias Mitigation through Bias Experts in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Existing approaches to mitigate the detrimental effect of bias on the network include debiasing methods that down-weight the biased examples identified by an auxiliary model, which is trained with explicit bias labels. |
| Approach: | They propose a framework that introduces binary classifiers between the auxiliary model and main model, coined bias experts, to reduce the detrimental effect of bias on the network. |
| Outcome: | The proposed approach outperforms the state-of-the-art on various datasets while achieving high performance on in-distribution data. |
Mitigating Shortcuts in Language Models with Soft Label Encoding (2024.lrec-main)
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| Challenge: | Recent studies have shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. |
| Approach: | They propose a framework for debiasing shortcuts and a dummy class to encode shortcuts into a model and use it to generate soft labels. |
| Outcome: | The proposed framework significantly improves out-of-distribution generalization while maintaining satisfactory in-district accuracy. |
FairFlow: Mitigating Dataset Biases through Undecided Learning for Natural Language Understanding (2024.emnlp-main)
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| Challenge: | Existing debiasing frameworks can detect known dataset biases and spurious correlations in data. |
| Approach: | They propose a framework that learns to be undecided in its predictions for data samples . they propose 'contrary' objective that learn debiased and robust representations from biased views . |
| Outcome: | The proposed framework outperforms existing methods against out-of-domain and hard test samples without compromising performance. |