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Language models (LMs) have greatly propelled the research on natural language processing.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, and et al. 2020 · 1901
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Nuanced metrics for measuring unintended bias with real data for text classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2019 · 1903
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Measuring and mitigating unintended bias in text classification
Lucas Dixon, John Li, Jeffrey Scott Sorensen, Nithum Thain, and Lucy Vasserman. 2018 · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clement Hongler. 2018 · 2018
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Cross-pair text representations for answer sentence selection
Kateryna Tymoshenko and Alessandro Moschitti. 2018 · 2018
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 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 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019 · 2019
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, and et al. 2020 · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, and et al. 2020 · 2020
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten. 2020 · 2020
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
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Detoxify
Laura Hanu and Unitary team. 2020 · 2020
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Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy. 2020 · 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 · 2020
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Hatecheck: Functional tests for hate speech detection models
Paul Röttger, Bertram Vidgen, Dong Nguyen, Zeerak Talat, Helen Z. Margetts, and Janet B. Pierrehumbert. 2020 · 2020
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Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 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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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, and et al. 2021 · 2021
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Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
Cited alongside, same era.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Membership inference attacks on machine learning: A survey
Hongsheng Hu, Zoran A. Salcic, Lichao Sun, Gillian Dobbie, P. Yu, and Xuyun Zhang. 2021 · 2021
Cited alongside, same era.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021 · 2021
Cited alongside, same era.
Leace: Perfect linear concept erasure in closed form
Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, and Stella Biderman. 2023 · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
BIG bench authors. 2023 · 2023
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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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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 Ahmed. 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D. Manning. 2021 · 2021
Cited alongside, same era.
Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi. 2021 · 2021
Cited alongside, same era.
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender, Emily Denton, and Alex Hanna. 2021 · 2021
Cited alongside, same era.
Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP
Timo Schick, Sahana Udupa, and Hinrich Schütze. 2021 · 2021
Cited alongside, same era.
Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021
Cited alongside, same era.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang. 2022 · 2022
Cited alongside, same era.
CRASS: A novel data set and benchmark to test counterfactual reasoning of large language models
Jörg Frohberg and Frank Binder. 2022 · 2022
Cited alongside, same era.
Nuno M. Guerreiro, Duarte Alves, Jonas Waldendorf, Barry Haddow, Alexandra Birch, Pierre Colombo, and André F. T. Martins. 2023 · 2023
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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. 2023 · 2023
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Xinshuo Hu, Dongfang Li, Zihao Zheng, Zhenyu Liu, Baotian Hu, and Min Zhang. 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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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2023 · 2023
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Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo. 2023 · 2023
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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. 2023 · 2023
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When do pre-training biases propagate to downstream tasks? a case study in text summarization
Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen McKeown, and Tatsunori Hashimoto. 2023 · 2023
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Nationality bias in text generation
Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar, Ting-Hao Huang, and Shomir Wilson. 2023 · 2023
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OpenAI. 2023 · 2023
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Task arithmetic in the tangent space: Improved editing of pre-trained models
Guillermo Ortiz-Jimenez, Alessandro Favero, and Pascal Frossard. 2023 · 2023
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Controlling the extraction of memorized data from large language models via prompt-tuning
Mustafa Ozdayi, Charith Peris, Jack FitzGerald, Christophe Dupuy, Jimit Majmudar, Haidar Khan, Rahil Parikh, and Rahul Gupta. 2023 · 2023
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Finedeb: A debiasing framework for language models
Akash Saravanan, Dhruv Mullick, Habibur Rahman, and Nidhi Hegde. 2023 · 2023
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Parameter efficient multi-task model fusion with partial linearization
Anke Tang, Li Shen, Yong Luo, Yibing Zhan, Han Hu, Bo Du, Yixin Chen, and Dacheng Tao. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, and et al. 2023 · 2023
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Machine unlearning of features and labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck. 2023 · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, and et al. 2023 · 2023
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Differentially private next-token prediction of large language models
James Flemings, Meisam Razaviyayn, and Murali Annavaram. 2024 · 2024
Closest in time.
Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin. 2024 · 2024
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