awesome-llm-unlearning
A resource repository for machine unlearning in large language models
Stars: 149
This repository tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.
README:
This repository tracks the latest research on machine unlearning in large language models (LLMs). The goal is to offer a comprehensive list of papers and resources relevant to the topic.
[!NOTE] If you believe your paper on LLM unlearning is not included, or if you find a mistake, typo, or information that is not up to date, please open an issue or submit a pull request, and I will be happy to update the list.
- An Adversarial Perspective on Machine Unlearning for AI Safety
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Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models
- Author(s): Anmol Mekala, Vineeth Dorna, Shreya Dubey, Abhishek Lalwani, David Koleczek, Mukund Rungta, Sadid Hasan, Elita Lobo
- Date: 2024-09
- Venue: -
- Code: -
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LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models
- Author(s): Akshaj Kumar Veldanda, Shi-Xiong Zhang, Anirban Das, Supriyo Chakraborty, Stephen Rawls, Sambit Sahu, Milind Naphade
- Date: 2024-09
- Venue: -
- Code: -
- MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts
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Unforgettable Generalization in Language Models
- Author(s): Eric Zhang, Leshem Chosen, Jacob Andreas
- Date: 2024-09
- Venue: COLM 2024
- Code: -
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Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
- Author(s): Md Rafi Ur Rashid, Jing Liu, Toshiaki Koike-Akino, Shagufta Mehnaz, Ye Wang
- Date: 2024-08
- Venue: -
- Code: -
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LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet
- Author(s): Nathaniel Li, Ziwen Han, Ian Steneker, Willow Primack, Riley Goodside, Hugh Zhang, Zifan Wang, Cristina Menghini, Summer Yue
- Date: 2024-08
- Venue: -
- Code: -
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Unlearning Trojans in Large Language Models: A Comparison Between Natural Language and Source Code
- Author(s): Mahdi Kazemi, Aftab Hussain, Md Rafiqul Islam Rabin, Mohammad Amin Alipour, Sen Lin
- Date: 2024-08
- Venue: -
- Code: -
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Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models
- Author(s): Hongbang Yuan, Zhuoran Jin, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao
- Date: 2024-08
- Venue: -
- Code: -
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A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction
- Author(s): Jiahui Gong, Jingtao Ding, Fanjin Meng, Guilong Chen, Hong Chen, Shen Zhao, Haisheng Lu, Yong Li
- Date: 2024-08
- Venue: -
- Code: -
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WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Models
- Author(s): Guitao Chen, Yunshen Wang, Hongye Sun, Guang Chen
- Date: 2024-08
- Venue: -
- Code: -
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Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models
- Author(s): Sungmin Cha, Sungjun Cho, Dasol Hwang, Moontae Lee
- Date: 2024-08
- Venue: -
- Code: -
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On Effects of Steering Latent Representation for Large Language Model Unlearning
- Author(s): Dang Huu-Tien, Trung-Tin Pham, Hoang Thanh-Tung, Naoya Inoue
- Date: 2024-08
- Venue: -
- Code: -
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Hotfixing Large Language Models for Code
- Author(s): Zhou Yang, David Lo
- Date: 2024-08
- Venue: -
- Code: -
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UNLEARN Efficient Removal of Knowledge in Large Language Models
- Author(s): Tyler Lizzo, Larry Heck
- Date: 2024-08
- Venue: -
- Code: -
- Tamper-Resistant Safeguards for Open-Weight LLMs
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On the Limitations and Prospects of Machine Unlearning for Generative AI
- Author(s): Shiji Zhou, Lianzhe Wang, Jiangnan Ye, Yongliang Wu, Heng Chang
- Date: 2024-08
- Venue: -
- Code: -
- Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models
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Demystifying Verbatim Memorization in Large Language Models
- Author(s): Jing Huang, Diyi Yang, Christopher Potts
- Date: 2024-07
- Venue: -
- Code: -
- Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective
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Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias Mitigation
- Author(s): Huimin Lu, Masaru Isonuma, Junichiro Mori, Ichiro Sakata
- Date: 2024-07
- Venue: -
- Cdoe: -
- Targeted Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
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What Makes and Breaks Safety Fine-tuning? A Mechanistic Study
- Author(s): Samyak Jain, Ekdeep Singh Lubana, Kemal Oksuz, Tom Joy, Philip H.S. Torr, Amartya Sanyal, Puneet K. Dokania
- Date: 2024-07
- Venue: -
- Code: -
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Practical Unlearning for Large Language Models
- Author(s): Chongyang Gao, Lixu Wang, Chenkai Weng, Xiao Wang, Qi Zhu
- Date: 2024-07
- Venue: -
- Code: -
- Learning to Refuse: Towards Mitigating Privacy Risks in LLMs
- Composable Interventions for Language Models
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MUSE: Machine Unlearning Six-Way Evaluation for Language Models
- Author(s): Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A. Smith, Chiyuan Zhang
- Date: 2024-07
- Venue: -
- Code: -
- If You Don't Understand It, Don't Use It: Eliminating Trojans with Filters Between Layers
- Safe Unlearning: A Surprisingly Effective and Generalizable Solution to Defend Against Jailbreak Attacks
- To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
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Can Small Language Models Learn, Unlearn, and Retain Noise Patterns?
- Author(s): Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani
- Date: 2024-07
- Venue: -
- Code: -
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UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
- Author(s): Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jimenez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, Eugene Bagdasaryan
- Date: 2024-07
- Venue: -
- Code: -
- PISTOL: Dataset Compilation Pipeline for Structural Unlearning of LLMs
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Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis
- Author(s): Weitao Ma, Xiaocheng Feng, Weihong Zhong, Lei Huang, Yangfan Ye, Xiachong Feng, Bing Qin
- Date: 2024-06
- Venue: -
- Code: -
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Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models
- Author(s): Dohyun Lee, Daniel Rim, Minseok Choi, Jaegul Choo
- Date: 2024-06
- Venue: ACL 2024 Findings
- Code: -
- Every Language Counts: Learn and Unlearn in Multilingual LLMs
- Mitigating Social Biases in Language Models through Unlearning
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Textual Unlearning Gives a False Sense of Unlearning
- Author(s): Jiacheng Du, Zhibo Wang, Kui Ren
- Date: 2024-06
- Venue: -
- Code: -
- Cross-Lingual Unlearning of Selective Knowledge in Multilingual Language Models
- SNAP: Unlearning Selective Knowledge in Large Language Models with Negative Instructions
- Soft Prompting for Unlearning in Large Language Models
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Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs
- Author(s): Swanand Ravindra Kadhe, Farhan Ahmed, Dennis Wei, Nathalie Baracaldo, Inkit Padhi
- Date: 2024-06
- Venue: -
- Code: -
- Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces
- Avoiding Copyright Infringement via Machine Unlearning
- RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
- REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space
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Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning
- Author(s): Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama
- Date: 2024-06
- Venue: -
- Code: -
- Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference
- Large Language Model Unlearning via Embedding-Corrupted Prompts
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Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning
- Author(s): Xuhan Zuo, Minghao Wang, Tianqing Zhu, Lefeng Zhang, Dayong Ye, Shui Yu, Wanlei Zhou
- Date: 2024-06
- Venue: -
- Code: -
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Cross-Modal Safety Alignment: Is textual unlearning all you need?
- Author(s): Trishna Chakraborty, Erfan Shayegani, Zikui Cai, Nael Abu-Ghazaleh, M. Salman Asif, Yue Dong, Amit K. Roy-Chowdhury, Chengyu Song
- Date: 2024-06
- Venue: -
- Code: -
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RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models
- Author(s): Bichen Wang, Yuzhe Zi, Yixin Sun, Yanyan Zhao, Bing Qin
- Date: 2024-06
- Venue: -
- Code: -
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Toward Robust Unlearning for LLMs
- Author(s): Rishub Tamirisa, Bhrugu Bharathi, Andy Zhou, Bo Li, Mantas Mazeika
- Date: 2024-05
- Venue: ICLR 2024 SeT-LLM Workshop
- Code: -
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Unlearning Climate Misinformation in Large Language Models
- Author(s): Michael Fore, Simranjit Singh, Chaehong Lee, Amritanshu Pandey, Antonios Anastasopoulos, Dimitrios Stamoulis
- Date: 2024-05
- Venue: -
- Code: -
- Large Scale Knowledge Washing
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Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
- Author(s): Jiaqi Li, Qianshan Wei, Chuanyi Zhang, Guilin Qi, Miaozeng Du, Yongrui Chen, Sheng Bi
- Date: 2024-05
- Venue: -
- Code: -
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To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models
- Author(s): George-Octavian Barbulescu, Peter Triantafillou
- Date: 2024-05
- Venue: ICML 2024
- Code: -
- SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
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Machine Unlearning in Large Language Models
- Author(s): Kongyang Chen, Zixin Wang, Bing Mi, Waixi Liu, Shaowei Wang, Xiaojun Ren, Jiaxing Shen
- Date: 2024-04
- Venue: -
- Code: -
- Offset Unlearning for Large Language Models
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Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
- Author(s): Weikai Lu, Ziqian Zeng, Jianwei Wang, Zhengdong Lu, Zelin Chen, Huiping Zhuang, Cen Chen
- Date: 2024-04
- Venue: -
- Code: -
- Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning
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Localizing Paragraph Memorization in Language Models
- Author(s): Niklas Stoehr, Mitchell Gordon, Chiyuan Zhang, Owen Lewis
- Date: 2024-03
- Venue: -
- Code: -
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The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
- Author(s): Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, Daniel Berrios, Alice Gatti, Justin D. Li, Ann-Kathrin Dombrowski, Shashwat Goel, Long Phan, Gabriel Mukobi, Nathan Helm-Burger, Rassin Lababidi, Lennart Justen, Andrew B. Liu, Michael Chen, Isabelle Barrass, Oliver Zhang, Xiaoyuan Zhu, Rishub Tamirisa, Bhrugu Bharathi, Adam Khoja, Zhenqi Zhao, Ariel Herbert-Voss, Cort B. Breuer, Samuel Marks, Oam Patel, Andy Zou, Mantas Mazeika, Zifan Wang, Palash Oswal, Weiran Lin, Adam A. Hunt, Justin Tienken-Harder, Kevin Y. Shih, Kemper Talley, John Guan, Russell Kaplan, Ian Steneker, David Campbell, Brad Jokubaitis, Alex Levinson, Jean Wang, William Qian, Kallol Krishna Karmakar, Steven Basart, Stephen Fitz, Mindy Levine, Ponnurangam Kumaraguru, Uday Tupakula, Vijay Varadharajan, Ruoyu Wang, Yan Shoshitaishvili, Jimmy Ba, Kevin M. Esvelt, Alexandr Wang, Dan Hendrycks
- Date: 2024-03
- Venue: -
- Code:
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Dissecting Language Models: Machine Unlearning via Selective Pruning
- Author(s): Nicholas Pochinkov, Nandi Schoots
- Date: 2024-03
- Venue: -
- Code: -
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Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models
- Author(s): Kang Gu, Md Rafi Ur Rashid, Najrin Sultana, Shagufta Mehnaz
- Date: 2024-03
- Venue: -
- Code: -
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Ethos: Rectifying Language Models in Orthogonal Parameter Space
- Author(s): Lei Gao, Yue Niu, Tingting Tang, Salman Avestimehr, Murali Annavaram
- Date: 2024-03
- Venue: -
- Code: -
- Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
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Guardrail Baselines for Unlearning in LLMs
- Author(s): Pratiksha Thaker, Yash Maurya, Virginia Smith
- Date: 2024-03
- Venue: ICLR 2024 SeT-LLM Workshop
- Code: -
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Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning
- Author(s): Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning
- Date: 2024-02
- Venue: -
- Code: -
- Unmemorization in Large Language Models via Self-Distillation and Deliberate Imagination
- Towards Safer Large Language Models through Machine Unlearning
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Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models
- Author(s): Lingzhi Wang, Xingshan Zeng, Jinsong Guo, Kam-Fai Wong, Georg Gottlob
- Date: 2024-02
- Venue: -
- Code: -
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Unlearnable Algorithms for In-context Learning
- Author(s): Andrei Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot
- Date: 2024-02
- Venue: -
- Code: -
- Machine Unlearning of Pre-trained Large Language Models
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Visual In-Context Learning for Large Vision-Language Models
- Author(s): Yucheng Zhou, Xiang Li, Qianning Wang, Jianbing Shen
- Date: 2024-02
- Venue: -
- Code: -
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EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
- Author(s): Shangyu Xing, Fei Zhao, Zhen Wu, Tuo An, Weihao Chen, Chunhui Li, Jianbing Zhang, Xinyu Dai
- Date: 2024-02
- Venue: -
- Code: -
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Unlearning Reveals the Influential Training Data of Language Models
- Author(s): Masaru Isonuma, Ivan Titov
- Date: 2024-01
- Venue: -
- Code: -
- TOFU: A Task of Fictitious Unlearning for LLMs
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FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs
- Author(s): Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo
- Date: 2023-12
- Venue: NeurIPS 2023 SoLaR Workshop
- Code: -
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Making Harmful Behaviors Unlearnable for Large Language Models
- Author(s): Xin Zhou, Yi Lu, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
- Date: 2023-11
- Venue: -
- Code: -
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Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models
- Author(s): Shiwen Ni, Dingwei Chen, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
- Date: 2023-11
- Venue: -
- Code: -
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Who's Harry Potter? Approximate Unlearning in LLMs
- Author(s): Ronen Eldan, Mark Russinovich
- Date: 2023-10
- Venue: -
- Code: -
- DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models
- Unlearn What You Want to Forget: Efficient Unlearning for LLMs
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In-Context Unlearning: Language Models as Few Shot Unlearners
- Author(s): Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
- Date: 2023-10
- Venue: -
- Code: -
- Large Language Model Unlearning
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Forgetting Private Textual Sequences in Language Models via Leave-One-Out Ensemble
- Author(s): Zhe Liu, Ozlem Kalinli
- Date: 2023-09
- Venue: -
- Code: -
- Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
- Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
- Unlearning Bias in Language Models by Partitioning Gradients
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Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data
- Author(s): Xinzhe Li, Ming Liu, Shang Gao
- Date: 2023-07
- Venue: -
- Code: -
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What can we learn from Data Leakage and Unlearning for Law?
- Author(s): Jaydeep Borkar
- Date: 2023-07
- Venue: -
- Code: -
- LEACE: Perfect linear concept erasure in closed form
- Composing Parameter-Efficient Modules with Arithmetic Operations
- KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
- Editing Models with Task Arithmetic
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Privacy Adhering Machine Un-learning in NLP
- Author(s): Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah, Dan Roth
- Date: 2022-12
- Venue: -
- Code: -
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The CRINGE Loss: Learning what language not to model
- Author(s): Leonard Adolphs, Tianyu Gao, Jing Xu, Kurt Shuster, Sainbayar Sukhbaatar, Jason Weston
- Date: 2022-11
- Venue: -
- Code: -
- Knowledge Unlearning for Mitigating Privacy Risks in Language Models
- Quark: Controllable Text Generation with Reinforced Unlearning
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Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions
- Author(s): Michele Miranda, Elena Sofia Ruzzetti, Andrea Santilli, Fabio Massimo Zanzotto, Sébastien Bratières, Emanuele Rodolà
- Date: 2024-08
- Venue: -
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Machine Unlearning in Generative AI: A Survey
- Author(s): Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, Meng Jiang
- Date: 2024-07
- Venue: -
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Digital Forgetting in Large Language Models: A Survey of Unlearning Methods
- Author(s): Alberto Blanco-Justicia, Najeeb Jebreel, Benet Manzanares, David Sánchez, Josep Domingo-Ferrer, Guillem Collell, Kuan Eeik Tan
- Date: 2024-04
- Venue: -
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Machine Unlearning for Traditional Models and Large Language Models: A Short Survey
- Author(s): Yi Xu
- Date: 2024-04
- Venue: -
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The Frontier of Data Erasure: Machine Unlearning for Large Language Models
- Author(s): Youyang Qu, Ming Ding, Nan Sun, Kanchana Thilakarathna, Tianqing Zhu, Dusit Niyato
- Date: 2024-03
- Venue: -
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Rethinking Machine Unlearning for Large Language Models
- Author(s): Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, Yang Liu
- Date: 2024-02
- Venue: -
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Eight Methods to Evaluate Robust Unlearning in LLMs
- Author(s): Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, Dylan Hadfield-Menell
- Date: 2024-02
- Venue: -
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Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges
- Author(s): Nianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, Weiqiang Zhang
- Date: 2023-11
- Venue: -
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Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions
- Author(s): Dawen Zhang, Pamela Finckenberg-Broman, Thong Hoang, Shidong Pan, Zhenchang Xing, Mark Staples, Xiwei Xu
- Date: 2023-07
- Venue: -
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Machine Unlearning in 2024
- Author(s): Ken Liu
- Date: 2024-05
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Deep Forgetting & Unlearning for Safely-Scoped LLMs
- Author(s): Stephen Casper
- Date: 2023-12
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This repository focuses on unraveling the sources that large language models tap into for attribution or citation. It delves into the origins of facts, their utilization by the models, the efficacy of attribution methodologies, and challenges tied to ambiguous knowledge reservoirs, biases, and pitfalls of excessive attribution.
context-cite
ContextCite is a tool for attributing statements generated by LLMs back to specific parts of the context. It allows users to analyze and understand the sources of information used by language models in generating responses. By providing attributions, users can gain insights into how the model makes decisions and where the information comes from.
weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.