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Although large language models (LLMs) have demonstrated remarkable proficiency in modeling text and generating human-like text, they may exhibit biases acquired from training data in doing so.
On the psychology of prediction
Daniel Kahneman and Amos Tversky · 1973
Earlier work this paper cites.
Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty
Amos Tversky and Daniel Kahneman · 1974
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Judgment under uncertainty: Heuristics and biases
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Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment
Amos Tversky and Daniel Kahneman · 1983
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Maya Bar-Hillel and Efrat Neter · 1993
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Stereotype threat
Steven J Spencer, Christine Logel, and Paul G Davies · 2016
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Semantics derived automatically from language corpora contain human-like biases
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Data statements for natural language processing: Toward mitigating system bias and enabling better science
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Word embeddings quantify 100 years of gender and ethnic stereotypes
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Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé, and Kate Crawford · 2018
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Gender bias in neural natural language processing
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What’s in a name? reducing bias in bios without access to protected attributes
Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, J. Chayes, C. Borgs, A. Chouldechova, S. Geyik, K. Kenthapadi, Anna Rumshisky, and A. Kalai · 2019
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The woman worked as a babysitter: On biases in language generation
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Gender bias in contextualized word embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang · 2019
Large language models are zero-shot reasoners
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Training language models to follow instructions with human feedback
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Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Taxonomy of risks posed by language models
Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel · 2022
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Large language models show human-like content biases in transmission chain experiments
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Language models are few-shot learners
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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
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Towards debiasing sentence representations
Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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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
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Mitigating political bias in language models through reinforced calibration
Ruibo Liu, Chenyan Jia, Jason Wei, Guangxuan Xu, Lili Wang, and Soroush Vosoughi · 2021
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Language models show human-like content effects on reasoning
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Large language models can self-improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han · 2022
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Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
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Should chatgpt be biased? challenges and risks of bias in large language models
Emilio Ferrara · 2023
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Bias against 93 stigmatized groups in masked language models and downstream sentiment classification tasks
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OpenAI · 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
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Self-consistency improves chain of thought reasoning in language models
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Do large language models show decision heuristics similar to humans? a case study using gpt-3.5
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