most citedMitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal

2 citations · 2 across the 1 of their papers we have counts for

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3 papers

cs.CL2022★ 2 cited

Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal

Umang Gupta, Jwala Dhamala, Varun Kumar +7

Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However,…

cs.LG2022

Measuring Fairness of Text Classifiers via Prediction Sensitivity

Satyapriya Krishna, Rahul Gupta, Apurv Verma +3

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have b…

cs.CL2021

Towards Realistic Single-Task Continuous Learning Research for NER

Justin Payan, Yuval Merhav, He Xie +4

There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications. Meanwhile, there is still a lack o…