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Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner.
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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Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018 · 1938
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Automatically identifying gender issues in machine translation using perturbations
Hila Gonen and Kellie Webster. 2020 · 1995
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2005
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Dialect diversity in text summarization on twitter
L Elisa Celis and Vijay Keswani. 2020 · 2007
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Gedi: Generative discriminator guided sequence generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2020 · 2009
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How to measure gender bias in machine translation: Optimal translators, multiple reference points
Anna Farkas and Renáta Németh. 2020 · 2011
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Decoding and diversity in machine translation
Nicholas Roberts, Davis Liang, Graham Neubig, and Zachary C Lipton. 2020 · 2011
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2020 · 2012
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Gender bias in multilingual neural machine translation: The architecture matters
Marta R Costa-jussà, Carlos Escolano, Christine Basta, Javier Ferrando, Roser Batlle, and Ksenia Kharitonova. 2020 · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
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Confronting abusive language online: A survey from the ethical and human rights perspective
Svetlana Kiritchenko, Isar Nejadgholi, and Kathleen C Fraser. 2020 · 2012
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M Bender, Emily Denton, and Alex Hanna. 2020 · 2012
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Vader: A parsimonious rule-based model for sentiment analysis of social media text
Clayton Hutto and Eric Gilbert. 2014 · 2014
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Unequal representation and gender stereotypes in image search results for occupations
Matthew Kay, Cynthia Matuszek, and Sean A Munson. 2015 · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
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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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The trouble with bias
Kate Crawford. 2017 · 2017
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Facebook apologizes after wrong translation sees Palestinian man arrested for posting ’good morning’
Thuy Ong. 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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Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M. Bender and Batya Friedman. 2018 · 2018
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Gender aware spoken language translation applied to english-arabic
Mostafa Elaraby, Ahmed Y Tawfik, Mahmoud Khaled, Hany Hassan, and Aly Osama. 2018 · 2018
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2018 · 2018
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Ethical challenges in data-driven dialogue systems
Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, and Joelle Pineau. 2018 · 2018
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Examining gender and race bias in two hundred sentiment analysis systems
Svetlana Kiritchenko and Saif Mohammad. 2018 · 2018
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Reducing gender bias in abusive language detection
Ji Ho Park, Jamin Shin, and Pascale Fung. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Getting gender right in neural machine translation
Eva Vanmassenhove, Christian Hardmeier, and Andy Way. 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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Identifying and reducing gender bias in word-level language models
Shikha Bordia and Samuel R. Bowman. 2019 · 2019
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On measuring gender bias in translation of gender-neutral pronouns
Won Ik Cho, Ji Won Kim, Seok Min Kim, and Nam Soo Kim. 2019 · 2019
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Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Equalizing gender bias in neural machine translation with word embeddings techniques
Joel Escudé Font and Marta R. Costa-jussà. 2019 · 2019
Cited alongside, same era.
Automatic gender identification and reinflection in Arabic
Nizar Habash, Houda Bouamor, and Christine Chung. 2019 · 2019
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Filling gender & number gaps in neural machine translation with black-box context injection
Automatically neutralizing subjective bias in text
Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, and Diyi Yang. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Reducing gender bias in neural machine translation as a domain adaptation problem
Danielle Saunders and Bill Byrne. 2020 · 2020
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Neural machine translation doesn’t translate gender coreference right unless you make it
Danielle Saunders, Rosie Sallis, and Bill Byrne. 2020 · 2020
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Predictive biases in natural language processing models: A conceptual framework and overview
Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. 2020 · 2020
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Amit Moryossef, Roee Aharoni, and Yoav Goldberg. 2019 · 2019
Cited alongside, same era.
Assessing gender bias in machine translation: a case study with google translate
Marcelo OR Prates, Pedro H Avelar, and Luís C Lamb. 2019 · 2019
Cited alongside, same era.
Reducing gender bias in word-level language models with a gender-equalizing loss function
Yusu Qian, Urwa Muaz, Ben Zhang, and Jae Won Hyun. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng. 2019 · 2019
Cited alongside, same era.
Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Towards Controllable Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Prem Natarajan, and Nanyun Peng. 2020 · 2020
Later among the works it cites.
Investigating societal biases in a poetry composition system
Emily Sheng and David Uthus. 2020 · 2020
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“you are grounded!”: Latent name artifacts in pre-trained language models
Vered Shwartz, Rachel Rudinger, and Oyvind Tafjord. 2020 · 2020
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Mitigating gender bias in machine translation with target gender annotations
Artūrs Stafanovičs, Mārcis Pinnis, and Toms Bergmanis. 2020 · 2020
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Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber. 2020 · 2020
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Defining and evaluating fair natural language generation
Catherine Yeo and Alyssa Chen. 2020 · 2020
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DIALOGPT : Large-scale generative pre-training for conversational response generation
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2020 · 2020
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Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
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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
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Towards cross-lingual generalization of translation gender bias
Won Ik Cho, Jiwon Kim, Jaeyeong Yang, and Nam Soo Kim. 2021 · 2021
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Improving gender translation accuracy with filtered self-training
Prafulla Kumar Choubey, Anna Currey, Prashant Mathur, and Georgiana Dinu. 2021 · 2021
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Bold: Dataset and metrics for measuring biases in open-ended language generation
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. 2021 · 2021
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Discovering and categorising language biases in reddit
Xavier Ferrer, Tom van Nuenen, Jose M Such, and Natalia Criado. 2021 · 2021
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How true is gpt-2? an empirical analysis of intersectional occupational biases
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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The truth is out there: Investigating conspiracy theories in text generation
Sharon Levy, Michael Saxon, and William Yang Wang. 2021 · 2021
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DExperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. 2021 · 2021
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Gender and representation bias in gpt-3 generated stories
Li Lucy and David Bamman. 2021 · 2021
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Honest: Measuring hurtful sentence completion in language models
Debora Nozza, Federico Bianchi, and Dirk Hovy. 2021 · 2021
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Investigating failures of automatic translation in the case of unambiguous gender
Adithya Renduchintala and Adina Williams. 2021 · 2021
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Re-imagining algorithmic fairness in india and beyond
Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, and Vinodkumar Prabhakaran. 2021 · 2021
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First the worst: Finding better gender translations during beam search
Danielle Saunders, Rosie Sallis, and Bill Byrne. 2021 · 2021
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Gender bias in machine translation
Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 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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”nice try, kiddo”: Investigating ad hominems in dialogue responses
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2021b · 2021
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Towards a comprehensive understanding and accurate evaluation of societal biases in pre-trained transformers
Andrew Silva, Pradyumna Tambwekar, and Matthew Gombolay. 2021 · 2021
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They, them, theirs: Rewriting with gender-neutral english
Tony Sun, Kellie Webster, Apu Shah, William Yang Wang, and Melvin Johnson. 2021 · 2021
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Understanding the capabilities, limitations, and societal impact of large language models
Alex Tamkin, Miles Brundage, Jack Clark, and Deep Ganguli. 2021 · 2021
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The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing
Marcus Tomalin, Bill Byrne, Shauna Concannon, Danielle Saunders, and Stefanie Ullmann. 2021 · 2021
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