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Pre-trained Language Models are widely used in many important real-world applications.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Intrinsic bias metrics do not correlate with application bias
Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, and Adam Lopez. 2021 · 1940
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CrowS-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020 · 1967
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Measuring and reducing gendered correlations in pre-trained models
Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed Chi, and Slav Petrov. 2020 · 2010
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Gender and dialect bias in YouTube’s automatic captions
Rachael Tatman. 2017 · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017 · 2017
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Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Feature-Wise Bias Amplification
Klas Leino, Matt Fredrikson, Emily Black, Shayak Sen, and Anupam Datta. 2019 · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger. 2019 · 2019
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What’s in a name? Reducing bias in bios without access to protected attributes
Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, and Adam Kalai. 2019 · 2019
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Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology
Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, and Ryan Cotterell. 2019 · 2019
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On measuring and mitigating biased inferences of word embeddings
Sunipa Dev, Tao Li, Jeff M Phillips, and Vivek Srikumar. 2020 · 2020
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Towards debiasing sentence representations
Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020 · 2020
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Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta. 2020 · 2020
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Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
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FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders
Pengyu Cheng, Weituo Hao, Siyang Yuan, Shijing Si, and Lawrence Carin. 2021 · 2021
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SimCSE: Simple contrastive learning of sentence embeddings
StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
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Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP
Timo Schick, Sahana Udupa, and Hinrich Schütze. 2021 · 2021
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On the intrinsic and extrinsic fairness evaluation metrics for contextualized language representations
Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, and Aram Galstyan. 2022 · 2022
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Auto-debias: Debiasing masked language models with automated biased prompts
Yue Guo, Yi Yang, and Ahmed Abbasi. 2022 · 2022
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MABEL: Attenuating gender bias using textual entailment data
Jacqueline He, Mengzhou Xia, Christiane Fellbaum, and Danqi Chen. 2022 · 2022
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Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Diverse adversaries for mitigating bias in training
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021 · 2021
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Debiasing pre-trained contextualised embeddings
Masahiro Kaneko and Danushka Bollegala. 2021 · 2021
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Sustainable modular debiasing of language models
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Debiasing isn’t enough! – on the effectiveness of debiasing MLMs and their social biases in downstream tasks
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P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022 · 2022
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An empirical survey of the effectiveness of debiasing techniques for pre-trained language models
Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy. 2022 · 2022
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Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing
Yibo Wang, Congying Xia, Guan Wang, and Philip S. Yu. 2022 · 2022
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ADEPT: A DEbiasing PrompT Framework
Ke Yang, Charles Yu, Yi Fung, Manling Li, and Heng Ji. 2022 · 2022
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PromptAttack: Probing dialogue state trackers with adversarial prompts
Xiangjue Dong, Yun He, Ziwei Zhu, and James Caverlee. 2023a · 2023
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Prompt tuning pushes farther, contrastive learning pulls closer: A two-stage approach to mitigate social biases
Yingji Li, Mengnan Du, Xin Wang, and Ying Wang. 2023 · 2023
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An empirical analysis of parameter-efficient methods for debiasing pre-trained language models
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