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Various studies have attempted to remove sensitive or private knowledge from a language model to prevent its unauthorized exposure.
Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Earlier work this paper cites.
Not just a black box: Interpretable deep learning by propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje. 2016 · 2016
Earlier work this paper cites.
Large language models sensitivity to the order of options in multiple-choice questions
Pouya Pezeshkpour and Estevam Hruschka. 2024 · 2017
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
You can teach an old dog new tricks! on training knowledge graph embeddings
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla. 2020 · 2020
Earlier work this paper cites.
Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur. 2022 · 2022
Earlier work this paper cites.
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2022 · 2022
Earlier work this paper cites.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023 · 2023
Earlier work this paper cites.
Shortcut learning of large language models in natural language understanding
Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao, and Xia Hu. 2023 · 2023
Earlier work this paper cites.
Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich. 2023 · 2023
Earlier work this paper cites.
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
Cited alongside, same era.
Large language models can be lazy learners: Analyze shortcuts in in-context learning
Ruixiang Tang, Dehan Kong, Longtao Huang, and Hui Xue. 2023 · 2023
Cited alongside, same era.
Task-specific compression for multi-task language models using attribution-based pruning
Nakyeong Yang, Yunah Jang, Hwanhee Lee, Seohyeong Jeong, and Kyomin Jung. 2023 · 2023
Cited alongside, same era.
Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2023 · 2023
Cited alongside, same era.
Large language models are not robust multiple choice selectors
Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. 2023 · 2023
Cited alongside, same era.
Gemma 2: Improving open language models at a practical size
Team Gemma, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. 2024 · 2024
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Rwku: Benchmarking real-world knowledge unlearning for large language models
Zhuoran Jin, Pengfei Cao, Chenhao Wang, Zhitao He, Hongbang Yuan, Jiachun Li, Yubo Chen, Kang Liu, and Jun Zhao. 2024 · 2024
Later among the works it cites.
How to turn your knowledge graph embeddings into generative models
Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, and Antonio Vergari. 2024 · 2024
Later among the works it cites.
Tofu: A task of fictitious unlearning for llms
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C Lipton, and J Zico Kolter. 2024 · 2024
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Mquake: Assessing knowledge editing in language models via multi-hop questions
Zexuan Zhong, Zhengxuan Wu, Christopher D Manning, Christopher Potts, and Danqi Chen. 2023 · 2023
Cited alongside, same era.
Explore spurious correlations at the concept level in language models for text classification
Yuhang Zhou, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, and Furong Huang. 2023 · 2023
Cited alongside, same era.
Improving few-shot generalization by exploring and exploiting auxiliary data
Alon Albalak, Colin A Raffel, and William Yang Wang. 2024 · 2024
Cited alongside, same era.
To each (textual sequence) its own: Improving memorized-data unlearning in large language models
George-Octavian Barbulescu and Peter Triantafillou. 2024 · 2024
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
Cited alongside, same era.
The wmdp benchmark: Measuring and reducing malicious use with unlearning
Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, Daniel Berrios, Alice Gatti, Justin D Li, Ann-Kathrin Dombrowski, Shashwat Goel, Long Phan, et al. 2024a
Cited in the paper.
Making long-context language models better multi-hop reasoners
Yanyang Li, Shuo Liang, Michael R Lyu, and Liwei Wang. 2024b
Cited in the paper.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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Muse: Machine unlearning six-way evaluation for language models
Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A Smith, and Chiyuan Zhang. 2024 · 2024
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To forget or not? towards practical knowledge unlearning for large language models
Bozhong Tian, Xiaozhuan Liang, Siyuan Cheng, Qingbin Liu, Mengru Wang, Dianbo Sui, Xi Chen, Huajun Chen, and Ningyu Zhang. 2024 · 2024
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Machine unlearning of pre-trained large language models
Jin Yao, Eli Chien, Minxin Du, Xinyao Niu, Tianhao Wang, Zezhou Cheng, and Xiang Yue. 2024 · 2024
Later among the works it cites.
Negative preference optimization: From catastrophic collapse to effective unlearning
Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. 2024 · 2024
Later among the works it cites.