The Rashomon effect is a mixed blessing in responsible machine learning.

Existing backdoor defense methods typically require either homogeneous assumption, validation datasets, or client optimization conflicts.

Single-view 3D reconstruction methods like Triplane Gaussian Splatting (TGS) have enabled high-quality 3D model generation from just a single image input within seconds.

Instead of relying on the LLM itself to unlearn, we enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget.

We employ a total of 26 annotators, consisting of 14 females and 12 males.

The acquisition of training data is crucial for machine learning applications.