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Large Language Models (LLMs) have made substantial progress in the past several months, shattering state-of-the-art benchmarks in many domains.
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
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Comprehending pronouns: A role for word-specific gender stereotype information
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Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) . Association for Computational Linguistics, New Orleans, Louisiana, 15–20
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Au pairs are rarely male: Norms on the gender perception of role names across English, French, and German
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WP:Clubhouse? An Exploration of Wikipedia’s Gender Imbalance. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (Mountain View, California) (WikiSym ’11) . Association for Computing Machinery, New York, NY, USA, 1–10
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Influences of grammatical and stereotypical gender during reading: eye movements in pronominal and noun phrase anaphor resolution
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Wikipedia, sociology, and the promise and pitfalls of Big Data
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Expectations of brilliance underlie gender distributions across academic disciplines
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Women are underrepresented in fields where success is believed to require brilliance
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Pragmatic relativity: Gender and context affect the use of personal pronouns in discourse differentially across languages
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Big Data’s Disparate Impact
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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
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Stereotypical Gender Effects in 2016
Margaret Grant, Hadas Kotek, Jayun Bae, and Jeffrey Lamontagne. 2016 · 2016
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Twenty years of stereotype threat research: A review of psychological mediators
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Can gender-fair language reduce gender stereotyping and discrimination?
Sabine Sczesny, Magda Formanowicz, and Franziska Moser. 2016 · 2016
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Stereotype threat
Steven J Spencer, Christine Logel, and Paul G Davies. 2016 · 2016
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Gender stereotypes about intellectual ability emerge early and influence children’s interests
Lin Bian, Sarah-Jane Leslie, and Andrei Cimpian. 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
Gender stereotypes and biases in early childhood: A systematic review
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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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Gender bias and stereotypes in linguistic example sentences
Hadas Kotek, Rikker Dockum, Sarah Babinski, and Christopher Geissler. 2021 · 2021
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StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . Association for Computational Linguistics, Online, 5356–5371
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Cited alongside, same era.
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2017 · 2017
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Gender and Dialect Bias in YouTube’s Automatic Captions. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing . Association for Computational Linguistics, Valencia, Spain, 53–59
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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Fairness Without Demographics in Repeated Loss Minimization
Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018 · 2018
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Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Svetlana Kiritchenko and Saif M. Mohammad. 2018 · 2018
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Microaggressions and Traumatic Stress: Theory, Research, and Clinical Treatment
K.L. Nadal. 2018 · 2018
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Probing Toxic Content in Large Pre-Trained Language Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . Association for Computational Linguistics, Online, 4262–4274
Nedjma Ousidhoum, Xinran Zhao, Tianqing Fang, Yangqiu Song, and Dit-Yan Yeung. 2021 · 2021
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The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies
Alexandre Blanco-Gonzalez, Alfonso Cabezon, Alejandro Seco-Gonzalez, Daniel Conde-Torres, Paula Antelo-Riveiro, Angel Pineiro, and Rebeca Garcia-Fandino. 2022 · 2022
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ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, Andreas Mittermeier, Anna Theresa Stüber, Johanna Topalis, Tobias Weber, Philipp Wesp, Bastian Sabel, Jens Ricke, and Michael Ingrisch. 2022 · 2022
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The Ghost in the Machine has an American accent: value conflict in GPT-3
Rebecca L Johnson, Giada Pistilli, Natalia Menédez-González, Leslye Denisse Dias Duran, Enrico Panai, Julija Kalpokiene, and Donald Jay Bertulfo. 2022 · 2022
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Pipelines for Social Bias Testing of Large Language Models. In Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models . Association for Computational Linguistics, virtual+Dublin, 68–74
Debora Nozza, Federico Bianchi, and Dirk Hovy. 2022 · 2022
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The Implications of OpenAI’s Assistant for Legal Services and Society
Andrew M Perlman. 2022 · 2022
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Large pre-trained language models contain human-like biases of what is right and wrong to do
Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A. Rothkopf, and Kristian Kersting. 2022 · 2022
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“I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 9180–9211
Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, and Adina Williams. 2022 · 2022
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You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings
Zeerak Talat, Aurélie Névéol, Stella Biderman, Miruna Clinciu, Manan Dey, Shayne Longpre, Sasha Luccioni, Maraim Masoud, Margaret Mitchell, Dragomir Radev, Shanya Sharma, Arjun Subramonian, Jaesung Tae, Samson Tan, Deepak Tunuguntla, and Oskar van der Wal. 2022 · 2022
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A Study of Implicit Bias in Pretrained Language Models against People with Disabilities. In Proceedings of the 29th International Conference on Computational Linguistics . International Committee on Computational Linguistics, Gyeongju, Republic of Korea, 1324–1332
Pranav Narayanan Venkit, Mukund Srinath, and Shomir Wilson. 2022 · 2022
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Who’s Afraid of ChatGPT? An Examination of ChatGPT’s Implications for Legal Writing
Ashley B Armstrong. 2023 · 2023
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Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, and Pascale Fung. 2023 · 2023
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Automation and Stock Prices: The Case of ChatGPT
Magnus Blomkvist, Yetaotao Qiu, and Yunfei Zhao. 2023 · 2023
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Speculative Futures on ChatGPT and Generative Artificial Intelligence (AI): A collective reflection from the educational landscape
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Deep reinforcement learning from human preferences
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ChatGPT for (finance) research: The Bananarama conjecture
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Towards Measuring the Representation of Subjective Global Opinions in Language Models
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Quantifying ChatGPT’s gender bias
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Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models
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Putting ChatGPT’s Medical Advice to the (Turing) Test: Survey Study
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ChatGPT, Professor of Law
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Ms. Categorized: Gender, notability, and inequality on Wikipedia
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Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity
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Nationality Bias in Text Generation
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