3.7k citations · 6.8k across the 18 of their papers we have counts for
7 papers · 1 filter
Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
Yangsibo Huang, Milad Nasr, Anastasios Angelopoulos +10
It is now common to evaluate Large Language Models (LLMs) by having humans manually vote to evaluate model outputs, in contrast to typical benchmarks that evaluate knowledge or ski…
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…
Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System
Daphne Ippolito, Nicholas Carlini, Katherine Lee +2
Neural language models are increasingly deployed into APIs and websites that allow a user to pass in a prompt and receive generated text. Many of these systems do not reveal genera…
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Daphne Ippolito, Florian Tramèr, Milad Nasr +5
Studying data memorization in neural language models helps us understand the risks (e.g., to privacy or copyright) associated with models regurgitating training data and aids in th…
Measuring Forgetting of Memorized Training Examples
Matthew Jagielski, Om Thakkar, Florian Tramèr +8
Machine learning models exhibit two seemingly contradictory phenomena: training data memorization, and various forms of forgetting. In memorization, models overfit specific trainin…
Quantifying Memorization Across Neural Language Models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski +3
Large language models (LMs) have been shown to memorize parts of their training data, and when prompted appropriately, they will emit the memorized training data verbatim. This is…
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts +6
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language proc…