5 papers
Exploring The Empirical Privacy Landscape of Synthetic Data
Zihao Zhao, Avi Schwarzschild, Casey Meehan +3
Methods for training language models on sensitive data differ in how they balance capability gains against the risk of leaking private information. In this paper, we present a cont…
Controlled Generation for Private Synthetic Text
Zihao Zhao, Anjalie Field
Text anonymization is essential for responsibly developing and deploying AI in high-stakes domains such as healthcare, social services, and law. In this work, we propose a novel me…
SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains
Krithika Ramesh, Daniel Smolyak, Zihao Zhao +4
We present SynthTextEval, a toolkit for conducting comprehensive evaluations of synthetic text. The fluency of large language model (LLM) outputs has made synthetic text potentiall…
AdaProb: Efficient Machine Unlearning via Adaptive Probability
Zihao Zhao, Yuchen Yang, Anjalie Field +1
Machine unlearning, enabling a trained model to forget specific data, is crucial for addressing erroneous data and adhering to privacy regulations like the General Data Protection…
RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning
Zihao Zhao, Yuchen Yang, Yijiang Li +1
The ripple effect poses a significant challenge in knowledge editing for large language models. Namely, when a single fact is edited, the model struggles to accurately update the r…