273 citations · 1.4k across the 55 of their papers we have counts for
6 papers · 1 filter
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
Ken Ziyu Liu, Christopher A. Choquette-Choo, Matthew Jagielski +4
An important question today is whether a given text was used to train a large language model (LLM). A \emph{completion} test is often employed: check if the LLM completes a suffici…
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training
Jaydeep Borkar, Matthew Jagielski, Katherine Lee +3
Due to the sensitive nature of personally identifiable information (PII), its owners may have the authority to control its inclusion or request its removal from large-language mode…
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…
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models
Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva +3
We address the challenge of ensuring differential privacy (DP) guarantees in training deep retrieval systems. Training these systems often involves the use of contrastive-style los…
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…