2 papers
cs.LG2026
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
BartÅomiej Marek, Lorenzo Rossi, Vincent Hanke +4
Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees. However, its practical effectiv…
cs.LG2024
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
Vincent Hanke, Tom Blanchard, Franziska Boenisch +3
While open Large Language Models (LLMs) have made significant progress, they still fall short of matching the performance of their closed, proprietary counterparts, making the latt…