Large Model

Large models, encompassing large language models (LLMs) and their multimodal counterparts (MLLMs), are rapidly advancing artificial intelligence by leveraging massive datasets and immense computational power to achieve state-of-the-art performance across diverse tasks. Current research emphasizes efficient fine-tuning techniques, including model compression and low-rank adaptation, to address the challenges of deploying these resource-intensive models, as well as improving their robustness and uncertainty quantification. These advancements are driving progress in various fields, from improved search engines and medical image analysis to novel applications in robotics, finance, and agriculture. The development of robust evaluation benchmarks and the exploration of the interplay between large and small models are also key areas of focus.