The current literature on memorization in Natural Language Models, especially Large Language Models (LLMs), poses severe security and privacy risks, as models tend to memorize personally identifying information (PIIs) from training data. We introduce Randomized Masked Fine-Tuning (RMFT), a novel privacy-preserving fine-tuning technique that reduces PII memorization while minimizing performance impact. Using the Enron Email Dataset, we demonstrate that RMFT achieves an 80.81% reduction in Total Extraction Rate and 80.17% reduction in Seen Extraction Rate compared to baseline fine-tuning, outperforming deduplication methods while maintaining only a 5.73% increase in perplexity. We present MaxTER, a Pareto-optimal evaluation framework for assessing privacy-utility tradeoffs, and show the performance of RMFT vs Deduplication by Area Under The Response Curve (AURC) metric.

In this section, we formalize and substantiate the claims of Theorem 1 . Theorem 1 has three parts, which we address in the following sections. First, in Section A.2, we show that the classifier makes progress during the early-learning phase: over the first We prove this rigorously in Section A.3, which shows that the overall magnitude of the gradient terms Finally, in Section A.4, we prove In terms of and ", the gradient ( 2) reads rL We will use the phrase "with high probability" to denote an event which happens with probability We will prove the claim by induction. We proceed with the induction. We now show that the classifier's accuracy on the mislabeled This proves the first claim.

We prove that early learning and memorization are fundamental phenomena in high-dimensional classification tasks, even in simple linear models, and give a theoretical explanation in this setting.

Diffusion models (DMs) produce very detailed and high-quality images.

We study finite sample expressivity, i.e., memorization power of ReLU networks.

Vision-Language Models (VLMs) have emerged as the state-of-the-art representation learning solution, with myriads of downstream applications such as image classification, retrieval and generation. A natural question is whether these models memorize their training data, which also has implications for generalization. We propose a new method for measuring memorization in VLMs, which we call d ej ` a vu memorization . For VLMs trained on image-caption pairs, we show that the model indeed retains information about individual objects in the training images beyond what can be inferred from correlations or the image caption. We evaluate d ej ` a vu memorization at both sample and population level, and show that it is significant for OpenCLIP trained on as many as 50M image-caption pairs. Finally, we show that text randomization considerably mitigates memorization while only moderately impacting the model's downstream task performance.

We again notice that larger models memorize training data faster. This section shows how perplexity and memorization on the special batch evolve over training. Figure 14 we see that perplexity continues to increase over training, while memorization flatlines. We show plots for the 1.3B model scale, although all of the experiments in 5 exhibit ( T 1) Figure 16 we analyze the average memory unit length over training for two model sizes. We notice that the larger 2.7B model has an average Exact training time varied depended on model scale and dataset size, but all models were trained for up to 140 hours.

The rate and extent to which a model memorizes its training data are key statistics that provide evidence about how it is likely to generalize to new test instances.

Of course, spotting near duplicates of training observations is only possible because these models yield realistic samples. This section describes additional details of the data sets, model architectures, and experimental setup. CIFAR-10 contains color images from 10 different categories and does not require further preprocessing. For CIFAR-10 and CelebA we used random horizontal flips during training as data augmentation. Full details of the model architecture are given in Table 1.

T ( x) = [CLS]x It was [MASK]. PLM to extract the label-related words from the whole unlabeled training corpus. We report the hyper-parameters in Table 2. Most of the hyper-parameters are the default parameters Thus, we provide insight into the effect of β, k and λ on the final results. We think the model may require more reference when there is no data for training. We will leave the engineering optimization about retrieval speed in our future work.