activity
20162024
most citedPaLM: Scaling Language Modeling with Pathways

2.1k citations · 3.9k across the 32 of their papers we have counts for

collaborators
Showing 2023Show all

7 papers · 1 filter

cs.CY2023★ 3 cited

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…

cs.LG2023★ 83 cited

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…

cs.LG2023★ 1 cited

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…

cs.CL2023★ 9 cited

Effective Prompt Extraction from Language Models

Yiming Zhang, Nicholas Carlini, Daphne Ippolito

The text generated by large language models is commonly controlled by prompting, where a prompt prepended to a user's query guides the model's output. The prompts used by companies…

cs.CL2023★ 25 cited

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…

cs.CL2023★ 7 cited

A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity

Shayne Longpre, Gregory Yauney, Emily Reif +8

Pretraining is the preliminary and fundamental step in developing capable language models (LM). Despite this, pretraining data design is critically under-documented and often guide…

cs.CR2023★ 100 cited

Extracting Training Data from Diffusion Models

Nicholas Carlini, Jamie Hayes, Milad Nasr +6

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images. In this w…