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Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis.
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Membership inference attacks on sequence-to-sequence models: Is my data in your machine translation system?
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Are Clinical BERT Models Privacy Preserving? The Difficulty of Extracting Patient-Condition Associations.. In HUMAN@ AAAI Fall Symposium
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On the importance of difficulty calibration in membership inference attacks
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Differentially private fine-tuning of language models
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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 · 2023
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Xiang Yue, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, and Sherman SM Chow. 2021 · 2021
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Counterfactual memorization in neural language models
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2022b · 2022
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MatSciBERT: A materials domain language model for text mining and information extraction
Tanishq Gupta, Mohd Zaki, NM Anoop Krishnan, and Mausam. 2022 · 2022
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Are Large Pre-Trained Language Models Leaking Your Personal Information?
Jie Huang, Hanyin Shao, and Kevin Chen-Chuan Chang. 2022 · 2022
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Active data pattern extraction attacks on generative language models
Bargav Jayaraman, Esha Ghosh, Huseyin Inan, Melissa Chase, Sambuddha Roy, and Wei Dai. 2022 · 2022
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Deduplicating training data mitigates privacy risks in language models. In International Conference on Machine Learning . PMLR, 10697–10707
Nikhil Kandpal, Eric Wallace, and Colin Raffel. 2022 · 2022
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When Does Differentially Private Learning Not Suffer in High Dimensions?
Xuechen Li, Daogao Liu, Tatsunori B Hashimoto, Huseyin A Inan, Janardhan Kulkarni, Yin-Tat Lee, and Abhradeep Guha Thakurta. 2022a · 2022
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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 · 2023
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OpenAI. 2023 · 2023
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Differentially Private In-Context Learning
Ashwinee Panda, Tong Wu, Jiachen T Wang, and Prateek Mittal. 2023 · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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Hanyin Shao, Jie Huang, Shen Zheng, and Kevin Chen-Chuan Chang. 2023 · 2023
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Detecting pretraining data from large language models
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Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin A Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, and Robert Sim. 2023 · 2023
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Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli. 2023 · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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From bert to gpt-3 codex: harnessing the potential of very large language models for data management
Immanuel Trummer. 2023 · 2023
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OmniscientDB: A Large Language Model-Augmented DBMS That Knows What Other DBMSs Do Not Know. In Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management . 1–7
Matthias Urban, Duc Dat Nguyen, and Carsten Binnig. 2023 · 2023
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KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, and Hongzhi Yin. 2023b · 2023
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Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2023 · 2023
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Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, and Huishuai Zhang. 2023a · 2023
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Bag of tricks for training data extraction from language models
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GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Pinjia He, Shuming Shi, and Zhaopeng Tu. 2023 · 2023
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Synthetic text generation with differential privacy: A simple and practical recipe
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TwHIN-BERT: A socially-enriched pre-trained language model for multilingual tweet representations at twitter. In Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining . 5597–5607
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Prompts should not be seen as secrets: Systematically measuring prompt extraction attack success
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Do Membership Inference Attacks Work on Large Language Models?
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