Dissociating language and thought in large language models

K Mahowald, AA Ivanova, IA Blank, N Kanwisher… - Trends in Cognitive …, 2024‏ - cell.com
Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …

Security and privacy challenges of large language models: A survey

BC Das, MH Amini, Y Wu - ACM Computing Surveys, 2025‏ - dl.acm.org
Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

[PDF][PDF] DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models.

B Wang, W Chen, H Pei, C **e, M Kang, C Zhang, C Xu… - NeurIPS, 2023‏ - blogs.qub.ac.uk
Abstract Generative Pre-trained Transformer (GPT) models have exhibited exciting progress
in their capabilities, capturing the interest of practitioners and the public alike. Yet, while the …

How to dp-fy ml: A practical guide to machine learning with differential privacy

N Ponomareva, H Hazimeh, A Kurakin, Z Xu… - Journal of Artificial …, 2023‏ - jair.org
Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Foundation models and fair use

P Henderson, X Li, D Jurafsky, T Hashimoto… - Journal of Machine …, 2023‏ - jmlr.org
Existing foundation models are trained on copyrighted material. Deploying these models
can pose both legal and ethical risks when data creators fail to receive appropriate …

Analyzing leakage of personally identifiable information in language models

N Lukas, A Salem, R Sim, S Tople… - … IEEE Symposium on …, 2023‏ - ieeexplore.ieee.org
Language Models (LMs) have been shown to leak information about training data through
sentence-level membership inference and reconstruction attacks. Understanding the risk of …

Memorization without overfitting: Analyzing the training dynamics of large language models

K Tirumala, A Markosyan… - Advances in …, 2022‏ - proceedings.neurips.cc
Despite their wide adoption, the underlying training and memorization dynamics of very
large language models is not well understood. We empirically study exact memorization in …

On the opportunities and risks of foundation models

R Bommasani, DA Hudson, E Adeli, R Altman… - arxiv preprint arxiv …, 2021‏ - arxiv.org
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

[HTML][HTML] A survey on large language model (llm) security and privacy: The good, the bad, and the ugly

Y Yao, J Duan, K Xu, Y Cai, Z Sun, Y Zhang - High-Confidence Computing, 2024‏ - Elsevier
Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …

Flocks of stochastic parrots: Differentially private prompt learning for large language models

H Duan, A Dziedzic, N Papernot… - Advances in Neural …, 2023‏ - proceedings.neurips.cc
Large language models (LLMs) are excellent in-context learners. However, the sensitivity of
data contained in prompts raises privacy concerns. Our work first shows that these concerns …