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The growing capability and availability of generative language models has enabled a wide range of new downstream tasks.
Mitigating gender bias in natural language processing: Literature review
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang. 2019 · 1906
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Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, et al. 2019 · 1908
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Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
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Multilingual generation and summarization of job adverts: The tree project
Harold Somers, Bill Black, Joakim Nivre, Torbjörn Lager, Annarosa Multari, Luca Gilardoni, Jeremy Ellman, and Alex Rogers. 1997 · 1997
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A Linguistic Comparison of Letters of Recommendation for Male and Female Chemistry and Biochemistry Job Applicants
Toni Schmader, Jessica Whitehead, and Vicki H Wysocki. 2007 · 2007
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Evidence That Gendered Wording in Job Advertisements Exists and Sustains Gender Inequality
Danielle Gaucher, Justin Friesen, and Aaron C. Kay. 2011 · 2011
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016 · 2016
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Round Up The Usual Suspects: Knowledge-Based Metaphor Generation
Tony Veale. 2016 · 2016
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Against discrimination: equality act 2010 (UK)
Elena Vladimirovna Fell and Maria Dyban. 2017 · 2017
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Connotation frames of power and agency in modern films
Maarten Sap, Marcella Cindy Prasettio, Ari Holtzman, Hannah Rashkin, and Yejin Choi. 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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Survey of the state of the art in natural language generation: Core tasks, applications and evaluation
Albert Gatt and Emiel Krahmer. 2018 · 2018
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Extending a parser to distant domains using a few dozen partially annotated examples
Vidur Joshi, Matthew Peters, and Mark Hopkins. 2018 · 2018
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Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words
Saif Mohammad. 2018 · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
Cited alongside, same era.
Attenuating bias in word vectors
Sunipa Dev and Jeff Phillips. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Measuring bias in contextualized word representations
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019 · 2019
Cited alongside, same era.
Artificial Intelligence, Machine Learning and EU Copyright Law: Who Owns AI?
Thomas Margoni. 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
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021 · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
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Hannah Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frederic A. Dreyer, Aleksandar Shtedritski, and Yuki M. Asano. 2021 · 2021
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How does counterfactually augmented data impact models for social computing constructs?
Indira Sen, Mattia Samory, Fabian Floeck, Claudia Wagner, and Isabelle Augenstein. 2021 · 2021
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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.
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 · 2020
Cited alongside, same era.
Workshop on Privacy in NLP (PrivateNLP 2020) , pages 903–904. Association for Computing Machinery, New York, NY, USA
Oluwaseyi Feyisetan, Sepideh Ghanavati, and Patricia Thaine. 2020 · 2020
Cited alongside, same era.
Teaching natural language processing through big data text summarization with problem-based learning
Liuqing Li, Jack Geissinger, William A. Ingram, and Edward A. Fox. 2020 · 2020
Cited alongside, same era.
Hiring now: A skill-aware multi-attention model for job posting generation
Liting Liu, Jie Liu, Wenzheng Zhang, Ziming Chi, Wenxuan Shi, and Yalou Huang. 2020 · 2020
Cited alongside, same era.
Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models
Eric Michael Smith and Adina Williams. 2021 · 2021
Later among the works it cites.
Process for adapting language models to society (palms) with values-targeted datasets
Irene Solaiman and Christy Dennison. 2021 · 2021
Later among the works it cites.
A survey on gender bias in natural language processing
Karolina Stanczak and Isabelle Augenstein. 2021 · 2021
Later among the works it cites.
Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al. 2021 · 2021
Later among the works it cites.
A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning
Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat, Wonsuk Yang, Hannah Rose Kirk, Aleksandar Shtedritski, and Max Bain. 2022 · 2022
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handsome
Cambridge University Dictionary. 2022 · 2022
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UK office for national statistics:employment by detailed occupation and industry by sex and age for great britain, UK and constituent countries
ONS. 2018 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et al. 2022 · 2022
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