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cs.AI2025
Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models
Soumi Das, Camila Kolling, Mohammad Aflah Khan +5
We study the inherent trade-offs in minimizing privacy risks and maximizing utility, while maintaining high computational efficiency, when fine-tuning large language models (LLMs).…
cs.AI2020★ 1 cited
Unifying Model Explainability and Robustness via Machine-Checkable Concepts
Vedant Nanda, Till Speicher, John P. Dickerson +2
As deep neural networks (DNNs) get adopted in an ever-increasing number of applications, explainability has emerged as a crucial desideratum for these models. In many real-world ta…