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Generating fair and accurate predictions plays a pivotal role in deploying large language models (LLMs) in the real world.
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
Earlier work this paper cites.
On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R Bowman, and Rachel Rudinger. 2019 · 1903
Earlier work this paper cites.
Gender bias in contextualized word embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cotterell, Vicente Ordonez, and Kai-Wei Chang. 2019 · 1904
Earlier work this paper cites.
Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology
Ran Zmigrod, Sabrina J Mielke, Hanna Wallach, and Ryan Cotterell. 2019 · 1906
Earlier work this paper cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019 · 1909
Earlier work this paper cites.
The nature of prejudice
Gordon Willard Allport, Kenneth Clark, and Thomas Pettigrew. 1954 · 1954
Earlier work this paper cites.
Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2020b · 2004
Earlier work this paper cites.
Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2004
Earlier work this paper cites.
Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitriy Pyrkin, Sergei Popov, and Artem Babenko. 2020 · 2004
Earlier work this paper cites.
Towards debiasing sentence representations
Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020 · 2007
Earlier work this paper cites.
Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2009
Earlier work this paper cites.
Unmasking contextual stereotypes: Measuring and mitigating bert’s gender bias
Marion Bartl, Malvina Nissim, and Albert Gatt. 2020 · 2010
Earlier work this paper cites.
Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R Bowman. 2020 · 2010
Earlier work this paper cites.
Measuring and reducing gendered correlations in pre-trained models
Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Ed Chi, and Slav Petrov. 2020 · 2010
Earlier work this paper cites.
Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2020 · 2012
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan. 2017 · 2017
Earlier work this paper cites.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Gender bias in coreference resolution
Rachel Rudinger, Jason Naradowsky, Brian Leonard, and Benjamin Van Durme. 2018 · 2018
Earlier work this paper cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
Earlier work this paper cites.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford et al. 2019 · 2019
Earlier work this paper cites.
On measuring and mitigating biased inferences of word embeddings
Sunipa Dev, Tao Li, Jeff M Phillips, and Vivek Srikumar. 2020 · 2020
Earlier work this paper cites.
Gpt-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti. 2020 · 2020
Earlier work this paper cites.
Towards a critical race methodology in algorithmic fairness
Alex Hanna, Emily Denton, Andrew Smart, and Jamila Smith-Loud. 2020 · 2020
Earlier work this paper cites.
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Earlier work this paper cites.
Fact: A diagnostic for group fairness trade-offs
Joon Sik Kim, Jiahao Chen, and Ameet Talwalkar. 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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Transformers: State-of-the-art natural language processing
Thomas Wolf et al. 2020 · 2020
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
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An interpretability illusion for bert
Tolga Bolukbasi, Adam Pearce, Ann Yuan, Andy Coenen, Emily Reif, Fernanda Viégas, and Martin Wattenberg. 2021 · 2021
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Aging with grace: Lifelong model editing with discrete key-value adaptors
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2022 · 2022
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Mabel: Attenuating gender bias using textual entailment data
Jacqueline He, Mengzhou Xia, Christiane Fellbaum, and Danqi Chen. 2022 · 2022
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Language generation models can cause harm: So what can we do about it? an actionable survey
Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, and Yulia Tsvetkov. 2022 · 2022
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Accuracy and fairness trade-offs in machine learning: A stochastic multi-objective approach
Suyun Liu and Luis Nunes Vicente. 2022 · 2022
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Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn. 2022 · 2022
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Pengyu Cheng, Weituo Hao, Siyang Yuan, Shijing Si, and Lawrence Carin. 2021 · 2021
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Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2021 · 2021
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, et al. 2021 · 2021
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Causal analysis of syntactic agreement mechanisms in neural language models
Matthew Finlayson, Aaron Mueller, Sebastian Gehrmann, Stuart Shieber, Tal Linzen, and Yonatan Belinkov. 2021 · 2021
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A survey on bias in deep nlp
Ismael Garrido-Muñoz, Arturo Montejo-Ráez, Fernando Martínez-Santiago, and L Alfonso Ureña-López. 2021 · 2021
Cited alongside, same era.
Diverse adversaries for mitigating bias in training
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021 · 2021
Cited alongside, same era.
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Fixing model bugs with natural language patches
Shikhar Murty, Christopher D Manning, Scott Lundberg, and Marco Tulio Ribeiro. 2022 · 2022
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Detecting unintended social bias in toxic language datasets
Nihar Sahoo, Himanshu Gupta, and Pushpak Bhattacharyya. 2022 · 2022
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Finding skill neurons in pre-trained transformer-based language models
Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang, Lei Hou, Zhiyuan Liu, and Juanzi Li. 2022 · 2022
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Hate speech and counter speech detection: Conversational context does matter
Xinchen Yu, Eduardo Blanco, and Lingzi Hong. 2022 · 2022
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Fast model debias with machine unlearning
Ruizhe Chen, Jianfei Yang, Huimin Xiong, Jianhong Bai, Tianxiang Hu, Jin Hao, Yang Feng, Joey Tianyi Zhou, Jian Wu, and Zuozhu Liu. 2023 · 2023
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Building stereotype repositories with llms and community engagement for scale and depth
Sunipa Dev, Akshita Jha, Jaya Goyal, Dinesh Tewari, Shachi Dave, and Vinodkumar Prabhakaran. 2023 · 2023
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Bias and fairness in large language models: A survey
Isabel O Gallegos, Ryan A Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K Ahmed. 2023 · 2023
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Dissecting recall of factual associations in auto-regressive language models
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023 · 2023
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Editing commonsense knowledge in gpt
Anshita Gupta, Debanjan Mondal, Akshay Krishna Sheshadri, Wenlong Zhao, Xiang Lorraine Li, Sarah Wiegreffe, and Niket Tandon. 2023 · 2023
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Transformer-patcher: One mistake worth one neuron
Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2023 · 2023
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Learning for counterfactual fairness from observational data
Jing Ma, Ruocheng Guo, Aidong Zhang, and Jundong Li. 2023 · 2023
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Biases in large language models: Origins, inventory and discussion
Roberto Navigli, Simone Conia, and Björn Ross. 2023 · 2023
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“i’m fully who i am”: Towards centering transgender and non-binary voices to measure biases in open language generation
Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard Zemel, and Rahul Gupta. 2023 · 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 · 2023
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Biasasker: Measuring the bias in conversational ai system
Yuxuan Wan, Wenxuan Wang, Pinjia He, Jiazhen Gu, Haonan Bai, and Michael R Lyu. 2023 · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, et al. 2023 · 2023
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An empirical analysis of parameter-efficient methods for debiasing pre-trained language models
Zhongbin Xie and Thomas Lukasiewicz. 2023 · 2023
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Adept: A debiasing prompt framework
Ke Yang, Charles Yu, Yi R Fung, Manling Li, and Heng Ji. 2023 · 2023
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Can we edit factual knowledge by in-context learning?
Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023 · 2023
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Faintbench: A holistic and precise benchmark for bias evaluation in text-to-image models
Hanjun Luo, Ziye Deng, Ruizhe Chen, and Zuozhu Liu. 2024 · 2024
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Theories of “gender” in nlp bias research
Hannah Devinney, Jenny Björklund, and Henrik Björklund. 2022 · 2083
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