Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities, but these systems are still known to hallucinate, and granular uncertainty estimation for long-form LLM generations remains challenging.

The retriever fetches candidate adapters that are relevant to the entire user prompt. Finally, the composer prunes and jointly categorizes the remaining adapters based on the prompt's

Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between textual and visual modalities to learn representations.

While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous perceptual quality scores remains largely unexplored.

MPI first trains model via masking and then employs pre-trained weight for high-quality zero-shot image denoising on a single noisy image.

To address these issues, we introduced JailTrickBench to evaluate the impact of various attack settings on LLM performance and provide a baseline for jailbreak attacks, encouraging the adoption of a standardized evaluation framework.