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As Large Language Models (LLMs) become widely adopted, understanding how they learn from, and memorize, training data becomes crucial.
A new measure of rank correlation
Maurice G Kendall · 1938
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer · 2016
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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The adverse effects of code duplication in machine learning models of code
Miltiadis Allamanis · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller · 2019
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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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, et al · 2020
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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Gutenberg scraper
Katherine Pully · 2020
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Understanding unintended memorization in federated learning
Om Thakkar, Swaroop Ramaswamy, Rajiv Mathews, and Françoise Beaufays · 2020
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Investigating the impact of pre-trained word embeddings on memorization in neural networks
Aleena Thomas, David Ifeoluwa Adelani, Ali Davody, Aditya Mogadala, and Dietrich Klakow · 2020
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Analyzing information leakage of updates to natural language models
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, and Marc Brockschmidt · 2020
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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What does it mean for a language model to preserve privacy?
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, and Florian Tramèr · 2022
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2022
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Glam: Efficient scaling of language models with mixture-of-experts
Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, et al · 2022
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Project cairaoke: Building the assistants of the future with breakthroughs in conversational ai, 2022
Alborz Geramifard · 2022
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Scaling laws and interpretability of learning from repeated data
Danny Hernandez, Tom Brown, Tom Conerly, Nova DasSarma, Dawn Drain, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Tom Henighan, Tristan Hume, et al · 2022
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Training compute-optimal large language models, 2022
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre · 2022
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Preventing verbatim memorization in language models gives a false sense of privacy
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A Choquette-Choo, and Nicholas Carlini · 2022
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Deduplicating training data mitigates privacy risks in language models
Nikhil Kandpal, Eric Wallace, and Colin Raffel · 2022
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini · 2022
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Data contamination: From memorization to exploitation
Inbal Magar and Roy Schwartz · 2022
Cited alongside, same era.
An empirical analysis of memorization in fine-tuned autoregressive language models
Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David K Evans, and Taylor Berg-Kirkpatrick · 2022
Cited alongside, same era.
Canary extraction in natural language understanding models
Rahil Parikh, Christophe Dupuy, and Rahul Gupta · 2022
Cited alongside, same era.
Defending against reconstruction attacks with r \ \backslash ’enyi differential privacy
Pierre Stock, Igor Shilov, Ilya Mironov, and Alexandre Sablayrolles · 2022
Cited alongside, same era.
Transformer memory as a differentiable search index
Yi Tay, Vinh Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, et al · 2022
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Revisiting relation extraction in the era of large language models
Somin Wadhwa, Silvio Amir, and Byron C Wallace · 2023
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OpenAI (2024) · 2024
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Llama 3 model card
AI@Meta · 2024
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Investigating data contamination in modern benchmarks for large language models
Chunyuan Deng, Yilun Zhao, Xiangru Tang, Mark Gerstein, and Arman Cohan · 2024
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Downstream task performance of bert models pre-trained using automatically de-identified clinical data
Thomas Vakili, Anastasios Lamproudis, Aron Henriksson, and Hercules Dalianis · 2022
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Semdedup: Data-efficient learning at web-scale through semantic deduplication
Amro Abbas, Kushal Tirumala, Dániel Simig, Surya Ganguli, and Ari S Morcos · 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, et al · 2023
Cited alongside, same era.
Phi-2: The surprising power of small language models
Mojan Javaheripi, Sébastien Bubeck, Marah Abdin, Jyoti Aneja, Sebastien Bubeck, Caio César Teodoro Mendes, Weizhu Chen, Allie Del Giorno, Ronen Eldan, Sivakanth Gopi, et al · 2023
Cited alongside, same era.
Mistral 7b, 2023
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
Cited alongside, same era.
Generalization or memorization: Data contamination and trustworthy evaluation for large language models
Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Bin Gu, Mengfei Yang, and Ge Li · 2024
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De-cop: Detecting copyrighted content in language models training data
André V Duarte, Xuandong Zhao, Arlindo L Oliveira, and Lei Li · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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https://www.ft.com/content/33328743-ba3b-470f-a2e3-f41c3a366613 , Apr 2024
FinancialTimes · 2024
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Investigating data contamination for pre-training language models
Minhao Jiang, Ken Ziyu Liu, Ming Zhong, Rylan Schaeffer, Siru Ouyang, Jiawei Han, and Sanmi Koyejo · 2024
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Madlad-400: A multilingual and document-level large audited dataset
Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, and Orhan Firat · 2024
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Publishers are selling papers to train ais-and making millions of dollars
Diana Kwon · 2024
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Biomistral: A collection of open-source pretrained large language models for medical domains, 2024
Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, and Richard Dufour · 2024
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Task contamination: Language models may not be few-shot anymore
Changmao Li and Jeffrey Flanigan · 2024
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Did the neurons read your book? document-level membership inference for large language models
Matthieu Meeus, Shubham Jain, Marek Rei, and Yves-Alexandre de Montjoye · 2024
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Copyright traps for large language models
Matthieu Meeus, Igor Shilov, Manuel Faysse, and Yves-Alexandre de Montjoye · 2024
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Proving test set contamination in black-box language models
Yonatan Oren, Nicole Meister, Niladri S Chatterji, Faisal Ladhak, and Tatsunori Hashimoto · 2024
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To the cutoff… and beyond? a longitudinal perspective on llm data contamination
Manley Roberts, Himanshu Thakur, Christine Herlihy, Colin White, and Samuel Dooley · 2024
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Evaluation data contamination in llms: how do we measure it and (when) does it matter?
Aaditya K Singh, Muhammed Yusuf Kocyigit, Andrew Poulton, David Esiobu, Maria Lomeli, Gergely Szilvasy, and Dieuwke Hupkes · 2024
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Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al · 2024
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D4: Improving llm pretraining via document de-duplication and diversification
Kushal Tirumala, Daniel Simig, Armen Aghajanyan, and Ari Morcos · 2024
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Proving membership in llm pretraining data via data watermarks
Johnny Tian-Zheng Wei, Ryan Yixiang Wang, and Robin Jia · 2024
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Doremi: Optimizing data mixtures speeds up language model pretraining
Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy S Liang, Quoc V Le, Tengyu Ma, and Adams Wei Yu · 2024
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To repeat or not to repeat: Insights from scaling llm under token-crisis
Fuzhao Xue, Yao Fu, Wangchunshu Zhou, Zangwei Zheng, and Yang You · 2024
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Privacy ripple effects from adding or removing personal information in language model training
Jaydeep Borkar, Matthew Jagielski, Katherine Lee, Niloofar Mireshghallah, David A Smith, and Christopher A Choquette-Choo · 2025
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Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models
Seyed Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, Oncel Tuzel, Samy Bengio, and Mehrdad Farajtabar · 2025
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Introducing ChatGPT
OpenAI · 2025
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