| Challenge: | Neural models exploit shallow surface features to perform language understanding tasks, rather than learning the deeper language understanding and reasoning skills that practitioners desire. |
| Approach: | They propose to use model debiasing techniques to pressure models away from spurious features and to use them to learn useful representations instead. |
| Outcome: | The proposed methods increase models' reliance on hidden biases instead of learning robust features that help them solve a task. |
Similar Papers
End-to-End Bias Mitigation by Modelling Biases in Corpora (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
| Approach: | They propose two learning strategies to train neural models that are more robust to dataset biases and transfer better to out-of-domain datasets. |
| Outcome: | The proposed methods improve robustness in all settings and transfer better to out-of-domain datasets. |
Debiasing Methods in Natural Language Understanding Make Bias More Accessible (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent debiasing methods in natural language understanding improve performance on out-of-distribution datasets by pressuring models into making unbiased predictions. |
| Approach: | They propose a general probing-based framework that allows for post-hoc interpretation of biases in language models and use an information-theoretic approach to measure the extractability of certain biase . |
| Outcome: | The proposed framework allows for post-hoc interpretation of biases in language models and measures the extractability of certain biase . |
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings (2025.coling-main)
Copied to clipboard
| Challenge: | Existing work in this field has looked most commonly into gender bias, racial bias, and religious bias. |
| Approach: | They propose an algorithm that uses a neural network to perform ‘soft debiasing’ and build on the seminal work of (CITATION) and (CitATION). |
| Outcome: | The proposed algorithm outperforms current methods on gender, race, and religion metrics on a wide range of metrics. |
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation (2024.emnlp-main)
Copied to clipboard
| Challenge: | In this study, we examine three considerations for intrinsic debiasing in neural machine translation models. |
| Approach: | They propose to measure the extrinsic bias of neural machine translation models by embedding them in a neural embeddable space and using different tokens to debias them. |
| Outcome: | The proposed methods over-rely on gender stereotypes and over-represent them in their models. |
Towards Debiasing NLU Models from Unknown Biases (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent proposed debiasing methods rely on the assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. |
| Approach: | They propose a framework that prevents models from mainly utilizing biases without knowing them in advance. |
| Outcome: | The proposed framework allows existing methods to retain performance improvement on challenge datasets without specifically targeting biases. |
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies show that pre-trained language models rely heavily on idiosyncratic biases of datasets. |
| Approach: | They propose a method which discourages models from exploiting biases while enabling them to receive enough incentive to learn from all the training examples. |
| Outcome: | The proposed method improves on out-of-distribution datasets while maintaining original in-district accuracy. |
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training (2020.emnlp-main)
Copied to clipboard
| Challenge: | Neural models pick up on annotation artefacts and spurious correlations, resulting in learning sentences that suffer from the same biases. |
| Approach: | They propose to tackle this problem by using adversarial training to reduce the bias in sentence representations by using an ensemble of adversaries. |
| Outcome: | The proposed approach produces more robust models outperforming previous de-biasing efforts when generalised to 12 other NLI datasets. |
Debiasing Masks: A New Framework for Shortcut Mitigation in NLU (2022.emnlp-main)
Copied to clipboard
| Challenge: | Debiasing language models from unwanted behaviors in natural language understanding datasets is a topic with increasing interest in the NLP community. |
| Approach: | They propose a method to debiase language models from unwanted behaviors in NLU tasks by identifying pruning masks that can be applied to a finetuned model. |
| Outcome: | The proposed method shows superior performance and performance over standard methods. |
Open-DeBias: Toward Mitigating Open-Set Bias in Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to addressing harmful biases in LLMs are limited to predefined categories . a novel, data-efficient, and parameter-efficient debiasing method is proposed to mitigate existing social and stereotypical biase . |
| Approach: | They propose an open-set bias detection and mitigation method to address harmful biases in text-based QA. |
| Outcome: | The proposed method improves QA accuracy on Korean BBQ dataset by nearly 48% on ambiguous subsets and 6% on disambiguated ones. |
Bias and Fairness in Natural Language Processing (D19-2)
Copied to clipboard
| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
| Outcome: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it aims to quantify and mitigate bias in natural language processing models for a wide spectrum of tasks . |