In response, several attempts have been made to protect the original images from such unauthorized data usage by adding imperceptible perturbations, which are designed to mislead the diffusion model and make it unable to properly generate new samples.

These were not included in the main paper due to space limitations. In Sec 4.1 of main paper, we have proposed a statistical model for the sensitive attribute classifier Generators are not completely biased. Given that a generator is trained on a reliable dataset with the availability of all classes of a given sensitive attribute, coupled with the advancement in generator's architecture, it is a fair assumption that the generator would learn some representation of each class in the sensitive attribute and not be completely Here, we provide more information on the necessary assumptions and the expanded forms of the equations. A.2, we will similarly provide more information on MLE value of Population Mean. A.1, we can equate the sample mean to the expanded theoretical model: µ Now given that the classifier's accuracy Fairness in generative models is defined as Equal Representation meaning that the generator is supposed to generate an equal number of samples for each element of an attribute, e.g., an equal number In the main paper Sec.3, we discussed that there could be considerable error in the fairness measurement, In our extended experiments in Sec.

In this work, we introduce ChatQA, a suite of models that outperform GPT -4 on retrieval-augmented generation (RAG) and conversational question answering (QA).

Transformer-based models, which have recently seen a surge in popularity due to their good performance and applicability to a variety of tasks, have a similar problem.

The resulting features are evaluated on k-nearest neighbor classification over 11 datasets from vision, 5 from natural language processing, and 2 from audio.