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Transformer-based pretrained large language models (PLM) such as BERT and GPT have achieved remarkable success in NLP tasks.
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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Stereotyping, prejudice, and discrimination
Susan T Fiske. 1998 · 1998
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Measuring individual differences in implicit cognition: the implicit association test
Anthony G Greenwald, Debbie E McGhee, and Jordan LK Schwartz. 1998 · 1998
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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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The problem with bias: Allocative versus representational harms in machine learning
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017 · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
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Learning to generate reviews and discovering sentiment
Alec Radford, Rafal Jozefowicz, and Ilya Sutskever. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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Learning gender-neutral word embeddings
Jieyu Zhao, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. 2018 · 2018
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What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
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Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Hila Gonen and Yoav Goldberg. 2019 · 2019
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Measuring bias in contextualized word representations
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019 · 2019
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Black is to criminal as Caucasian is to police: Detecting and removing multiclass bias in word embeddings
Thomas Manzini, Lim Yao Chong, Alan W Black, and Yulia Tsvetkov. 2019 · 2019
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Gender-preserving debiasing for pre-trained word embeddings
Kaneko Masahiro and D Bollegala. 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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Are sixteen heads really better than one?
Paul Michel, Omer Levy, and Graham Neubig. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019 · 2019
Cited alongside, same era.
Assessing social and intersectional biases in contextualized word representations
Yi Chern Tan and L Elisa Celis. 2019 · 2019
Cited alongside, same era.
Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
Cited alongside, same era.
Social biases in NLP models as barriers for persons with disabilities
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. 2020 · 2020
Cited alongside, same era.
Towards debiasing sentence representations
Entropy-based attention regularization frees unintended bias mitigation from lists
Giuseppe Attanasio, Debora Nozza, Dirk Hovy, and Elena Baralis. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 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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Controlling bias exposure for fair interpretable predictions
Zexue He, Yu Wang, Julian McAuley, and Bodhisattwa Prasad Majumder. 2022 · 2022
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Benchmarking intersectional biases in nlp
John P Lalor, Yi Yang, Kendall Smith, Nicole Forsgren, and Ahmed Abbasi. 2022 · 2022
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Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020 · 2020
Cited alongside, same era.
A primer in BERTology: What we know about how BERT works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2020
Cited alongside, same era.
Masked language model scoring
Julian Salazar, Davis Liang, Toan Q. Nguyen, and Katrin Kirchhoff. 2020 · 2020
Cited alongside, same era.
Predictive biases in natural language processing models: A conceptual framework and overview
Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. 2020 · 2020
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
Fairfil: Contrastive neural debiasing method for pretrained text encoders
Pengyu Cheng, Weituo Hao, Siyang Yuan, Shijing Si, and Lawrence Carin. 2021 · 2021
Cited alongside, same era.
Discovering and categorising language biases in reddit
Xavier Ferrer, Tom van Nuenen, Jose M Such, and Natalia Criado. 2021 · 2021
Cited alongside, same era.
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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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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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 · 2022
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Evaluating correctness and faithfulness of instruction-following models for question answering
Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, and Siva Reddy. 2023 · 2023
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Exploring the feasibility of chatgpt for event extraction
Jun Gao, Huan Zhao, Changlong Yu, and Ruifeng Xu. 2023 · 2023
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Bo Li, Gexiang Fang, Yang Yang, Quansen Wang, Wei Ye, Wen Zhao, and Shikun Zhang. 2023 · 2023
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The political biases of chatgpt
David Rozado. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Zero-shot information extraction via chatting with chatgpt
Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, et al. 2023 · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
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Causal-debias: Unifying debiasing in pretrained language models and fine-tuning via causal invariant learning
Fan Zhou, Yuzhou Mao, Liu Yu, Yi Yang, and Ting Zhong. 2023 · 2023
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