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Large language models (LLMs) have garnered significant attention for their remarkable performance in a continuously expanding set of natural language processing 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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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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A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition
Susan T Fiske, Amy JC Cuddy, Peter Glick, and Jun Xu. 2002 · 2002
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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The trouble with bias
Kate Crawford. 2017 · 2017
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hdbscan: Hierarchical density based clustering
Leland McInnes, John Healy, and Steve Astels. 2017 · 2017
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Commonsense knowledge aware conversation generation with graph attention
Hao Zhou, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018 · 2018
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KagNet: Knowledge-aware graph networks for commonsense reasoning
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, and Xiang Ren. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 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. 2019a · 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. 2019b · 2019
Cited alongside, same era.
Rˆ3: Reverse, retrieve, and rank for sarcasm generation with commonsense knowledge
Tuhin Chakrabarty, Debanjan Ghosh, Smaranda Muresan, and Nanyun Peng. 2020 · 2020
Cited alongside, same era.
“you are grounded!”: Latent name artifacts in pre-trained language models
Vered Shwartz, Rachel Rudinger, and Oyvind Tafjord. 2020 · 2020
Cited alongside, same era.
Stereotyping norwegian salmon: An inventory of pitfalls in fairness benchmark datasets
Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models
Hannah Rose Kirk, Yennie Jun, Filippo Volpin, Haider Iqbal, Elias Benussi, Frederic Dreyer, Aleksandar Shtedritski, and Yuki Asano. 2021 · 2021
Later among the works it cites.
Lawyers are dishonest? quantifying representational harms in commonsense knowledge resources
Ninareh Mehrabi, Pei Zhou, Fred Morstatter, Jay Pujara, Xiang Ren, and Aram Galstyan. 2021b · 2021
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StereoSet: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2021 · 2021
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Language models as or for knowledge bases
Simon Razniewski, Andrew Yates, Nora Kassner, and Gerhard Weikum. 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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Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach. 2021a · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Five sources of bias in natural language processing
Dirk Hovy and Shrimai Prabhumoye. 2021 · 2021
Cited alongside, same era.
Stereotyping Norwegian salmon: An inventory of pitfalls in fairness benchmark datasets
Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, and Hanna Wallach. 2021b
Cited in the paper.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021a
Cited in the paper.
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Bertopic: Neural topic modeling with a class-based tf-idf procedure
Maarten Grootendorst. 2022 · 2022
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Quantifying social biases using templates is unreliable
Preethi Seshadri, Pouya Pezeshkpour, and Sameer Singh. 2022 · 2022
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Crawling the internal knowledge-base of language models
Roi Cohen, Mor Geva, Jonathan Berant, and Amir Globerson. 2023 · 2023
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