273 citations · 1.1k across the 56 of their papers we have counts for
6 papers · 1 filter
Measuring Non-Adversarial Reproduction of Training Data in Large Language Models
Michael Aerni, Javier Rando, Edoardo Debenedetti +3
Large language models memorize parts of their training data. Memorizing short snippets and facts is required to answer questions about the world and to be fluent in any language. B…
Forcing Diffuse Distributions out of Language Models
Yiming Zhang, Avi Schwarzschild, Nicholas Carlini +2
Despite being trained specifically to follow user instructions, today's instructiontuned language models perform poorly when instructed to produce random outputs. For example, when…
Query-Based Adversarial Prompt Generation
Jonathan Hayase, Ema Borevkovic, Nicholas Carlini +2
Recent work has shown it is possible to construct adversarial examples that cause an aligned language model to emit harmful strings or perform harmful behavior. Existing attacks wo…
Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo +8
Large language models are now tuned to align with the goals of their creators, namely to be "helpful and harmless." These models should respond helpfully to user questions, but ref…
Counterfactual Memorization in Neural Language Models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee +3
Modern neural language models that are widely used in various NLP tasks risk memorizing sensitive information from their training data. Understanding this memorization is important…
Deduplicating Training Data Makes Language Models Better
Katherine Lee, Daphne Ippolito, Andrew Nystrom +4
We find that existing language modeling datasets contain many near-duplicate examples and long repetitive substrings. As a result, over 1% of the unprompted output of language mode…