Instead of relying on the LLM itself to unlearn, we enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget.

One concerning capability of these models is their ability to alter small attributes of an original image.

Nevertheless, it is unclear how improved representation learning can benefit reinforcement learning from human feedback on language models.

Despite their strong performances on many generative tasks, diffusion models require a large number of sampling steps in order to generate realistic samples.

Image Signal Processors (ISPs) play a fundamental role in camera systems.

Limitations in either capability can impede the overall performance of a VLM. A systematic evaluation of the perception and reasoning capabilities is crucial to provide valuable insights for future model optimization.

Neural Radiance Fields (NeRFs) have shown remarkable performances in producing novel-view images from high-quality scene images.

The web has become the primary communal source of data, or "data commons", for general-purpose

Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale.