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Found 4 papers, 1 papers with code
Bias Begets Bias: The Impact of Biased Embeddings on Diffusion Models
By specifically adapting the perspective of embedding spaces, we establish new fairness conditions for diffusion model development and evaluation.
Critical windows: non-asymptotic theory for feature emergence in diffusion models
Additionally, preliminary experiments on Stable Diffusion suggest critical windows may serve as a useful tool for diagnosing fairness and privacy violations in real-world diffusion models.
Pandora's White-Box: Precise Training Data Detection and Extraction in Large Language Models
In fine-tuning, we find that a simple attack based on the ratio of the loss between the base and fine-tuned models is able to achieve near-perfect MIA performance; we then leverage our MIA to extract a large fraction of the fine-tuning dataset from fine-tuned Pythia and Llama models.
MoPe: Model Perturbation-based Privacy Attacks on Language Models
In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training data of a pre-trained language model, given white-box access to the models parameters.
