2 papers
cs.CL2026
MAPLE: Metadata Augmented Private Language Evolution
Eli Chien, Yuzheng Hu, Ryan McKenna +3
Differentially private (DP) fine-tuning of large language models (LLMs) requires massive compute and full model access, which rules out state-of-the-art proprietary APIs for genera…
cs.LG2025
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning
Rongzhe Wei, Mufei Li, Mohsen Ghassemi +7
Large Language Models (LLMs) embed sensitive, human-generated data, prompting the need for unlearning methods. Although certified unlearning offers strong privacy guarantees, its r…