We provide the guidelines presented to the users for the creation of the dataset. To see some examples of how the guidelines can be applied, visit the examples document. You can use it to rate each guideline and leave feedback for each task. The user should be allowed to refuse to give up any information. Ask the user to elaborate or rephrase instead.

In Figure 1, we compare our LMC framework with the baseline Softmax, and present qualitative results on the TinyImageNet dataset. Below, we discuss them in more detail. AUROC is a widely-used threshold-independent evaluation metric. Both authors contributed equally to the work. Before entering the inference process, similar to our framework, Softmax also pre-stores certain CLIP and DINO features to make the inference process more efficient.