Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

Large language models (LLMs) have pushed the limits of natural language understanding
and exhibited excellent problem-solving ability. Despite the great success, most existing …

To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up
decoder inference. However, MQA can lead to quality degradation, and moreover it may not …

Widely used alignment techniques, such as reinforcement learning from human feedback
(RLHF), rely on the ability of humans to supervise model behavior-for example, to evaluate …

Knowledge Distillation (KD) is a promising technique for reducing the high computational
demand of large language models (LLMs). However, previous KD methods are primarily …

Recent works have shown that chain-of-thought (CoT) prompting can elicit language models
to solve complex reasoning tasks, step-by-step. However, prompt-based CoT methods are …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

For years, the YOLO series has been the de facto industry-level standard for efficient object
detection. The YOLO community has prospered overwhelmingly to enrich its use in a …