Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications. W atermark-ing, a technique that aims to embed information in the output of a model to verify its source, is useful for mitigating the misuse of such AI-generated content. However, we show that common design choices in LLM watermarking schemes make the resulting systems surprisingly susceptible to attack--leading to fundamental trade-offs in robustness, utility, and usability. To navigate these trade-offs, we rigorously study a set of simple yet effective attacks on common watermarking systems, and propose guidelines and defenses for LLM watermarking in practice.

The Segmentation Anything Model (SAM) requires labor-intensive data labeling. We present Unsupervised SAM (UnSAM) for promptable and automatic whole-image segmentation that does not require human annotations.

The new study of almost 900 dogs aims to help owners pinpoint breathing issues. Breakthroughs, discoveries, and DIY tips sent six days a week. Despite their popularity, for their seemingly helpless-looking eyes and flat faces, short-skulled (or brachycephalic) dogs like the French bulldog often have serious difficulty breathing. A study published today in the journal found that in 12 breeds, a flat face, collapsing nostrils, and rounded physique puts them at a higher risk for developing common breathing conditions. Pekingese and Japanese chins were noted to be the highest risk.

Then it fine-tunes the LLM to maximize the learned reward using RL techniques.

In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation.

State-of-the-art image restoration methods currently face challenges in terms of computational requirements and performance, making them impractical for deployment on edge devices such as phones and resource-limited devices.

A significant task in statistics and machine learning currently revolves around generating samples from a target distribution that is only accessible via a dataset.