83 citations · 101 across the 10 of their papers we have counts for
10 papers
Playing Whack-a-Mole with misconceptions about memorization, extraction, and copyright
A. Feder Cooper
After careful review, I'm confident the headline fine-tuning memorization results in Alignment Whack-a-Mole use an invalid measurement procedure. The book memorization coverage met…
Probabilistic "Copies" in Generative AI Models
Mark A. Lemley, A. Feder Cooper
Recent work shows that it is possible to extract verbatim or near-verbatim text of some copyrighted works from some large language models (LLMs or models). That is evidence that th…
Extractable Memorization From First Principles
A. Feder Cooper, Marika Swanberg, Jamie Hayes +6
Recent work on extractable memorization in language models suffers from two contrasting validity problems. Some studies overstate extraction, for example, by using sequences too sh…
Estimating near-verbatim extraction risk in language models with decoding-constrained beam search
A. Feder Cooper, Mark A. Lemley, Christopher De Sa +6
Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -…
Exploring the limits of strong membership inference attacks on large language models
Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo +13
State-of-the-art membership inference attacks (MIAs) typically require training many reference models, making it difficult to scale these attacks to large pre-trained language mode…
The Files are in the Computer: On Copyright, Memorization, and Generative AI
A. Feder Cooper, James Grimmelmann
The New York Times's copyright lawsuit against OpenAI and Microsoft alleges OpenAI's GPT models have "memorized" NYT articles. Other lawsuits make similar claims. But parties, cour…
Stealing Part of a Production Language Model
Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham +11
We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI's ChatGPT or Google's PaLM-2. Speci…
Report of the 1st Workshop on Generative AI and Law
A. Feder Cooper, Katherine Lee, James Grimmelmann +32
This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw), held in July 2023. A cross-disciplinary group of practitioners and scholars from com…
Scalable Extraction of Training Data from (Production) Language Models
Milad Nasr, Nicholas Carlini, Jonathan Hayase +7
This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training da…
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images
Aaron Gokaslan, A. Feder Cooper, Jasmine Collins +6
We assemble a dataset of Creative-Commons-licensed (CC) images, which we use to train a set of open diffusion models that are qualitatively competitive with Stable Diffusion 2 (SD2…