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Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material.
Binary codes capable of correcting deletions, insertions, and reversals
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On the resemblance and containment of documents
Andrei Z Broder · 1997
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman · 2017
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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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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 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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Fair learning
Mark A Lemley and Bryan Casey · 2021
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4:22-cv-06823, N.D. Cal. 2022
DOE 1 v. GitHub, Inc · 2022
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A dataset auditing method for collaboratively trained machine learning models
Yangsibo Huang, Chun-Yin Huang, Xiaoxiao Li, and Kai Li · 2022
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2022
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Booksum: A collection of datasets for long-form narrative summarization
Wojciech Kryściński, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong, and Dragomir Radev · 2022
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Continual learning and private unlearning
Bo Liu, Qiang Liu, and Peter Stone · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
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Unrolling sgd: Understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
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Self-rag: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi · 2023
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The foundation model transparency index
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, and Percy Liang · 2023
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang · 2023
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Speak, memory: An archaeology of books known to chatgpt/gpt-4
Kent Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman · 2023
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What’s in my big data?
Yanai Elazar, Akshita Bhagia, Ian Helgi Magnusson, Abhilasha Ravichander, Dustin Schwenk, Alane Suhr, Evan Pete Walsh, Dirk Groeneveld, Luca Soldaini, Sameer Singh, Hanna Hajishirzi, Noah A. Smith, and Jesse Dodge · 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, et al · 2023
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Depn: Detecting and editing privacy neurons in pretrained language models
Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, and Deyi Xiong · 2023
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Retrieval-augmented multimodal language modeling
Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Richard James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, and Wen-Tau Yih · 2023
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Counterfactual memorization in neural language models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, and Nicholas Carlini · 2023
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System prompts
Anthropic · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Ronen Eldan and Mark Russinovich · 2023
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3:23-cv-04625, (N.D. Cal.), 2023
Chabon v. OpenAI, Inc., · 2023
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3:23-cv-03417, 2023
Kadrey v. Meta Platforms, Inc · 2023
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1:23-cv-11195, 2023
The New York Times Company v. Microsoft Corporation · 2023
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23-cv-03416-AMO, (N.D. Cal.), 2023
Tremblay v. OpenAI, Inc., · 2023
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Configuring GitHub Copilot settings on GitHub.com
GitHub · 2023
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Foundation models and fair use
Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A Lemley, and Percy Liang · 2023
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Copybench: Measuring literal and non-literal reproduction of copyright-protected text in language model generation
Tong Chen, Akari Asai, Niloofar Mireshghallah, Sewon Min, James Grimmelmann, Yejin Choi, Hannaneh Hajishirzi, Luke Zettlemoyer, and Pang Wei Koh · 2024
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How to protect copyright data in optimization of large language models?
Timothy Chu, Zhao Song, and Chiwun Yang · 2024
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The faiss library
Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazaré, Maria Lomeli, Lucas Hosseini, and Hervé Jégou · 2024
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Be like a goldfish, don’t memorize! mitigating memorization in generative llms
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Fantastic copyrighted beasts and how (not) to generate them
Luxi He, Yangsibo Huang, Weijia Shi, Tinghao Xie, Haotian Liu, Yue Wang, Luke Zettlemoyer, Chiyuan Zhang, Danqi Chen, and Peter Henderson · 2024
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Automatic jailbreaking of the text-to-image generative ai systems
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Talkin”bout ai generation: Copyright and the generative-ai supply chain (the short version)
Katherine Lee, A Feder Cooper, and James Grimmelmann · 2024
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The wmdp benchmark: Measuring and reducing malicious use with unlearning
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RA-DIT: Retrieval-augmented dual instruction tuning
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Shield: Evaluation and defense strategies for copyright compliance in llm text generation
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Data portraits: Recording foundation model training data
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Introducing dbrx: A new state-of-the-art open llm
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Consent and compensation: Resolving generative ai’s copyright crisis
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Safety alignment should be made more than just a few tokens deep, 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Gemma 2: Improving open language models at a practical size
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Negative preference optimization: From catastrophic collapse to effective unlearning
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Judging llm-as-a-judge with mt-bench and chatbot arena
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