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Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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"ROUGE: A Package for Automatic Evaluation of Summaries"
Chin-Yew Lin. 2004 · 2004
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang. 2015 · 2015
Earlier work this paper cites.
"A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories"
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Membership Inference Attacks against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Earlier work this paper cites.
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 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 · 2019
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Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto. 2020 · 2020
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Machine Unlearning of Features and Labels
Alexander Warnecke, Lukas Pirch, Christian Wressnegger, and Konrad Rieck. 2021 · 2021
Earlier work this paper cites.
Evaluating Machine Unlearning via Epistemic Uncertainty
Alexander Becker and Thomas Liebig. 2022 · 2022
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Continual learning and private unlearning
Bo Liu, Qiang Liu, and Peter Stone. 2022 · 2022
Earlier work this paper cites.
QUARK: Controllable text generation with reinforced unlearning
Ximing Lu, Sean Welleck, Jack Hessel, Liwei Jiang, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, and Yejin Choi. 2022 · 2022
Cited alongside, same era.
Unrolling SGD: Understanding factors influencing Machine Unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot. 2022 · 2022
Cited alongside, same era.
Identifying and mitigating the security risks of generative ai
Clark Barrett, Brad Boyd, Elie Bursztein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, et al. 2023 · 2023
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
BIG bench authors. 2023 · 2023
Cited alongside, same era.
Unlearn what you want to forget: Efficient unlearning for LLMs
Jiaao Chen and Diyi Yang. 2023 · 2023
Cited alongside, same era.
"ANALOGICAL - A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models"
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Bimal Gajera, Shreeyash Gowaikar, Chandan Gupta, Aman Chadha, Aishwarya Naresh Reganti, Amit Sheth, and Amitava Das. 2023 · 2023
Later among the works it cites.
The Rise and Potential of Large Language Model Based Agents: A Survey
Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wensen Cheng, Qi Zhang, Wenjuan Qin, Yongyan Zheng, Xipeng Qiu, Xuanjing Huang, and Tao Gui. 2023 · 2023
Later among the works it cites.
"Unlearning Bias in Language Models by Partitioning Gradients"
Charles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, and Heng Ji. 2023 · 2023
Later among the works it cites.
Digital forgetting in large language models: A survey of unlearning methods
Alberto Blanco-Justicia, Najeeb Jebreel, Benet Manzanares, David Sánchez, Josep Domingo-Ferrer, Guillem Collell, and Kuan Eeik Tan. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng, and Chen Wang. 2023 · 2023
Cited alongside, same era.
An overview of Catastrophic AI Risks
Dan Hendrycks, Mantas Mazeika, and Thomas Woodside. 2023 · 2023
Cited alongside, same era.
Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2023 · 2023
Cited alongside, same era.
Model Sparsity Can Simplify Machine Unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu. 2023 · 2023
Cited alongside, same era.
Textbooks Are All You Need II: phi-1.5 technical report
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, and Yin Tat Lee. 2023 · 2023
Cited alongside, same era.
Leveraging Large Language Models for Multiple Choice Question Answering
Joshua Robinson and David Wingate. 2023 · 2023
Cited alongside, same era.
Knowledge unlearning for llms: Tasks, methods, and challenges
Nianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, and Weiqiang Zhang. 2023 · 2023
Cited alongside, same era.
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, Parker Schuh, Kensen Shi, Sashank Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2024 · 2024
Closest in time.
Unmemorization in large language models via self-distillation and deliberate imagination
Yijiang River Dong, Hongzhou Lin, Mikhail Belkin, Ramon Huerta, and Ivan Vulić. 2024 · 2024
Closest in time.
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, 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, and Dan Hendrycks. 2024 · 2024
Closest in time.
Rethinking Machine Unlearning for Large Language Models
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, and Yang Liu. 2024 · 2024
Closest in time.
TOFU: A Task of Fictitious Unlearning for LLMs
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter. 2024 · 2024
Closest in time.
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, and Wenhu Chen. 2024 · 2024
Closest in time.
Long-form Factuality in Large Language Models
Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, and Quoc V. Le. 2024 · 2024
Closest in time.
"On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models"
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, and Amitava Das. 2024 · 2024
Closest in time.
Large Language Model Unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2024 · 2024
Closest in time.