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Found 3 papers, 1 papers with code
Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
Therefore, parameter-efficient fine-tuning such as LoRA have emerged, allowing users to fine-tune LLMs without the need for considerable computing resources, with little performance degradation compared to fine-tuning all parameters.
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored.
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
Differentially private synthetic data is a promising alternative for sensitive data release.
