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Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years.
Techniques for data hiding
Walter Bender, Daniel F. Gruhl, Norishige Morimoto, and Anthony Lu · 1995
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Electronic marking and identification techniques to discourage document copying
J.T. Brassil, S. Low, N.F. Maxemchuk, and L. O’Gorman · 1995
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Secure multi-party computation
Oded Goldreich · 1998
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Copyright protection for the electronic distribution of text documents
Jack Brassil, Steven H. Low, and Nicholas F. Maxemchuk · 1999
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Information hiding-a survey
F.A.P. Petitcolas, R.J. Anderson, and M.G. Kuhn · 1999
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Applications for data hiding
Walter Bender, William Butera, Daniel F. Gruhl, Raymond Hwang, Fernando J. Paiz, and Sofya Pogreb · 2000
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Natural language watermarking and tamperproofing
Mikhail J. Atallah, Victor Raskin, Christian F. Hempelmann, Mercan Karahan, Radu Sion, Umut Topkara, and Katrina E. Triezenberg · 2002
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Towards linguistic steganography: A systematic investigation of approaches, systems, and issues
Richard Bergmair · 2004
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Natural language watermarking
Mercan Topkara, Cüneyt M. Taskiran, and Edward J. Delp · 2005
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Graph laplacian regularization for large-scale semidefinite programming
Kilian Q Weinberger, Fei Sha, Qihui Zhu, and Lawrence Saul · 2006
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Relationships and data sanitization: A study in scarlet
Matt Bishop, Justin Cummins, Sean Peisert, Anhad Singh, Bhume Bhumiratana, Deborah Agarwal, Deborah Frincke, and Michael Hogarth · 2010
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Word length based zero-watermarking algorithm for tamper detection in text documents
Zunera Jalil, Anwar M Mirza, and Hajira Jabeen · 2010
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Content based zero-watermarking algorithm for authentication of text documents
Zunera Jalil, Anwar M Mirza, and Maria Sabir · 2010
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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A study of various steganographic techniques used for information hiding
C.P.Sumathi, T.Santanam, Graduate School of Science, Sdnb Vaishnav College For Women, Chennai, Indian Institute of Science, DG Vaishnav College For Men, and India · 2013
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A survey of digital watermarking techniques, applications and attacks
Prabhishek Singh and Ramneet Singh Chadha · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
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Copyright for web content using invisible text watermarking
Nighat Mir · 2014
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Secure multiparty computation
Ronald Cramer, Ivan Bjerre Damgård, et al · 2015
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Machine learning applications in cancer prognosis and prediction
Konstantina Kourou, Themis P Exarchos, Konstantinos P Exarchos, Michalis V Karamouzis, and Dimitrios I Fotiadis · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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An innovative technique for web text watermarking (aitw)
Milad Taleby Ahvanooey, Hassan Dana Mazraeh, and Seyed Hashem Tabasi · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth · 2017
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Su Lin Blodgett and Brendan O’Connor · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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The trouble with bias, 2017
Kate Crawford · 2017
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Conscientious classification: A data scientist’s guide to discrimination-aware classification
Brian d’Alessandro, Cathy O’Neil, and Tom LaGatta · 2017
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 2018
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A comparative analysis of information hiding techniques for copyright protection of text documents
Milad Taleby Ahvanooey, Qianmu Li, Hiuk Jae Shim, and Yanyan Huang · 2018
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Improving language understanding by generative pre-training, 2018
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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General data protection regulation (gdpr)
General Data Protection Regulation · 2018
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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
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Regulation of artificial intelligence in the united states
John Frank Weaver · 2018
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Fairness and Machine Learning: Limitations and Opportunities
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
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How stereotypes are shared through language: a review and introduction of the aocial categories and stereotypes communication (scsc) framework
Camiel J Beukeboom and Christian Burgers · 2019
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Towards intelligent regulation of artificial intelligence
Miriam C Buiten · 2019
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Gltr: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush · 2019
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Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
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Does gender matter? towards fairness in dialogue systems
Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu, and Jiliang Tang · 2019
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RoBERTa: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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It’s all in the name: Mitigating gender bias with name-based counterfactual data substitution
Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel · 2019
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On measuring social biases in sentence encoders
Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, and Rachel Rudinger · 2019
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2019
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Smpai: Secure multi-party computation for federated learning
Vaikkunth Mugunthan, Antigoni Polychroniadou, David Byrd, and Tucker Hybinette Balch · 2019
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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
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Analyzing privacy loss in updates of natural language models
Shruti Tople, Marc Brockschmidt, Boris Köpf, Olga Ohrimenko, and Santiago Zanella-Béguelin · 2019
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A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
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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 · 2019
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Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach · 2020
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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
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Artificial moral agents: A survey of the current status
José-Antonio Cervantes, Sonia López, Luis-Felipe Rodríguez, Salvador Cervantes, Francisco Cervantes, and Félix Ramos · 2020
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On measuring and mitigating biased inferences of word embeddings
Sunipa Dev, Tao Li, Jeff M Phillips, and Vivek Srikumar · 2020
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Data-driven distributionally robust electric vehicle balancing for mobility-on-demand systems under demand and supply uncertainties
Sihong He, Lynn Pepin, Guang Wang, Desheng Zhang, and Fei Miao · 2020
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Membership inference attacks on sequence-to-sequence models: Is my data in your machine translation system?
Sorami Hisamoto, Matt Post, and Kevin Duh · 2020
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Reducing sentiment bias in language models via counterfactual evaluation
Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, and Pushmeet Kohli · 2020
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Hate speech detection and racial bias mitigation in social media based on bert model
Marzieh Mozafari, Reza Farahbakhsh, and Noël Crespi · 2020
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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
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Input-aware dynamic backdoor attack
Tuan Anh Nguyen and Anh Tran · 2020
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Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg · 2020
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Do neural ranking models intensify gender bias?
Navid Rekabsaz and Markus Schedl · 2020
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Information leakage in embedding models
Congzheng Song and Ananth Raghunathan · 2020
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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
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An interpretable mortality prediction model for covid-19 patients
Li Yan, Hai-Tao Zhang, Jorge Goncalves, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, Xiuchuan Tang, Liang Jing, Mingyang Zhang, et al · 2020
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Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Farooqi, and James Zou · 2021
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Mitigating language-dependent ethnic bias in BERT
Jaimeen Ahn and Alice Oh · 2021
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Large-scale differentially private bert
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi · 2021
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A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al · 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
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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 · 2021
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Socially responsible ai algorithms: Issues, purposes, and challenges
Lu Cheng, Kush R Varshney, and Huan Liu · 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
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Safelearn: Secure aggregation for private federated learning
Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Helen Möllering, Thien Duc Nguyen, Phillip Rieger, Ahmad-Reza Sadeghi, Thomas Schneider, Hossein Yalame, et al · 2021
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Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases
Wei Guo and Aylin Caliskan · 2021
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Diverse adversaries for mitigating bias in training
Xudong Han, Timothy Baldwin, and Trevor Cohn · 2021
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Detect and perturb: Neutral rewriting of biased and sensitive text via gradient-based decoding
Zexue He, Bodhisattwa Prasad Majumder, and Julian McAuley · 2021
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Learning and evaluating a differentially private pre-trained language model
Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Peled-Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, et al · 2021
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Generating gender augmented data for NLP
Nishtha Jain, Maja Popović, Declan Groves, and Eva Vanmassenhove · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Text-free prosody-aware generative spoken language modeling
Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu-Anh Nguyen, Morgane Rivière, Abdelrahman Mohamed, Emmanuel Dupoux, et al · 2021
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
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Towards understanding and mitigating social biases in language models
Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov · 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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Dataset inference: Ownership resolution in machine learning
Pratyush Maini, Mohammad Yaghini, and Nicolas Papernot · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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HONEST: Measuring hurtful sentence completion in language models
Debora Nozza, Federico Bianchi, and Dirk Hovy · 2021
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Post-processing for individual fairness
Felix Petersen, Debarghya Mukherjee, Yuekai Sun, and Mikhail Yurochkin · 2021
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Societal biases in retrieved contents: Measurement framework and adversarial mitigation of bert rankers
Navid Rekabsaz, Simone Kopeinik, and Markus Schedl · 2021
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The chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation
Huw Roberts, Josh Cowls, Jessica Morley, Mariarosaria Taddeo, Vincent Wang, and Luciano Floridi · 2021
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Selective differential privacy for language modeling
Weiyan Shi, Aiqi Cui, Evan Li, Ruoxi Jia, and Zhou Yu · 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
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A framework for understanding sources of harm throughout the machine learning life cycle
Harini Suresh and John Guttag · 2021
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NeuTral Rewriter: A rule-based and neural approach to automatic rewriting into gender neutral alternatives
Eva Vanmassenhove, Chris Emmery, and Dimitar Shterionov · 2021
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Adversarial glue: A multi-task benchmark for robustness evaluation of language models
Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, and Bo Li · 2021
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Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al · 2021
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Challenges in detoxifying language models
Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, and Po-Sen Huang · 2021
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Protecting your nlg models with semantic and robust watermarks
Tao Xiang, Chunlong Xie, Shangwei Guo, Jiwei Li, and Tianwei Zhang · 2021
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To be robust or to be fair: Towards fairness in adversarial training
Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang · 2021
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al · 2021
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A survey on federated learning
Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao · 2021
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Twitter sentiment analysis of covid vaccines
Wenbo Zhu and Tiechuan Hu · 2021
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Entropy-based attention regularization frees unintended bias mitigation from lists
Giuseppe Attanasio, Debora Nozza, Dirk Hovy, and Elena Baralis · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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Lamp: Extracting text from gradients with language model priors
Mislav Balunovic, Dimitar Dimitrov, Nikola Jovanović, and Martin Vechev · 2022
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
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The-x: Privacy-preserving transformer inference with homomorphic encryption
Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, and Furu Wei · 2022
Cited alongside, same era.
Panning for gold in federated learning: Targeted text extraction under arbitrarily large-scale aggregation
Hong-Min Chu, Jonas Geiping, Liam H Fowl, Micah Goldblum, and Tom Goldstein · 2022
Cited alongside, same era.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
Cited alongside, same era.
A unified evaluation of textual backdoor learning: Frameworks and benchmarks
Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, and Maosong Sun · 2022
Cited alongside, same era.
Fairdistillation: mitigating stereotyping in language models
Pieter Delobelle and Bettina Berendt · 2022
Gpt understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2023
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Coco: Coherence-enhanced machine-generated text detection under low resource with contrastive learning
Xiaoming Liu, Zhaohan Zhang, Yichen Wang, Hang Pu, Yu Lan, and Chao Shen · 2023
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Trustworthy llms: a survey and guideline for evaluating large language models’ alignment
Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li · 2023
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Watermarking text data on large language models for dataset copyright protection
Yixin Liu, Hongsheng Hu, Xuyun Zhang, and Lichao Sun · 2023
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Breaking the trilemma of privacy, utility, efficiency via controllable machine unlearning
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Cited alongside, same era.
Decepticons: Corrupted transformers breach privacy in federated learning for language models
Liam Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojtek Czaja, Micah Goldblum, and Tom Goldstein · 2022
Cited alongside, same era.
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention
Yacine Gaci, Boualem Benattallah, Fabio Casati, and Khalid Benabdeslem · 2022
Cited alongside, same era.
Watermarking pre-trained language models with backdooring
Chenxi Gu, Chengsong Huang, Xiaoqing Zheng, Kai-Wei Chang, and Cho-Jui Hsieh · 2022
Cited alongside, same era.
Recovering private text in federated learning of language models
Samyak Gupta, Yangsibo Huang, Zexuan Zhong, Tianyu Gao, Kai Li, and Danqi Chen · 2022
Cited alongside, same era.
Mitigating gender bias in distilled language models via counterfactual role reversal
Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, and Aram Galstyan · 2022
Cited alongside, same era.
Detoxifying text with marco: Controllable revision with experts and anti-experts
Skyler Hallinan, Alisa Liu, Yejin Choi, and Maarten Sap · 2022
Cited alongside, same era.
Balancing out bias: Achieving fairness through balanced training
Xudong Han, Timothy Baldwin, and Trevor Cohn · 2022
Cited alongside, same era.
Zheyuan Liu, Guangyao Dou, Yijun Tian, Chunhui Zhang, Eli Chien, and Ziwei Zhu · 2023
Later among the works it cites.
Gpts don’t keep secrets: Searching for backdoor watermark triggers in autoregressive language models
Evan Lucas and Timothy Havens · 2023
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Backdoor attacks against transformers with attention enhancement
Weimin Lyu, Songzhu Zheng, Haibin Ling, and Chao Chen · 2023
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Attention-enhancing backdoor attacks against bert-based models
Weimin Lyu, Songzhu Zheng, Lu Pang, Haibin Ling, and Chao Chen · 2023
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Split-and-denoise: Protect large language model inference with local differential privacy
Peihua Mai, Ran Yan, Zhe Huang, Youjia Yang, and Yan Pang · 2023
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Membership inference attacks against language models via neighbourhood comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick · 2023
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How much do language models copy from their training data? evaluating linguistic novelty in text generation using raven
R Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, and Asli Celikyilmaz · 2023
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Using in-context learning to improve dialogue safety
Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, and Dilek Hakkani-Tür · 2023
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Concerns about using a digital mask to safeguard patient privacy
Matthieu Meeus, Shubham Jain, and Yves-Alexandre de Montjoye · 2023
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The imperative for regulatory oversight of large language models (or generative ai) in healthcare
Bertalan Meskó and Eric J Topol · 2023
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The challenges for regulating medical use of chatgpt and other large language models
Timo Minssen, Effy Vayena, and I Glenn Cohen · 2023
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Detectgpt: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, and Chelsea Finn · 2023
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Nationality bias in text generation
Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar, Ting-Hao Huang, and Shomir Wilson · 2023
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Scalable extraction of training data from (production) language models
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A Feder Cooper, Daphne Ippolito, Christopher A Choquette-Choo, Eric Wallace, Florian Tramèr, and Katherine Lee · 2023
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Capabilities of gpt-4 on medical challenge problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz · 2023
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BLIND: Bias removal with no demographics
Hadas Orgad and Yonatan Belinkov · 2023
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Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, and William Yang Wang · 2023
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Never too late to learn: Regularizing gender bias in coreference resolution
SunYoung Park, Kyuri Choi, Haeun Yu, and Youngjoong Ko · 2023
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Artificial intelligence, algorithmic recommendation and decision-making in european union law:: analysis of the regulatory challenge and legal certainty
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