5 papers
Hierarchical Dual-Subspace Decoupling for Continual Learning in Vision-Language Models
Mengxin Qin, Xiang Zhang, Kun Wei +2
Class-incremental learning aims to continuously acquire new knowledge while preserving previously learned information, thereby mitigating catastrophic forgetting. Existing methods…
DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models
Mengxin Qin, Xiang Zhang, Xi Wang +3
Continual learning enables vision-language models to accumulate knowledge and adapt to evolving tasks without retraining from scratch. However, in multi-domain task-incremental lea…
Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models
Yanbin Yin, Kun Zhou, Zhen Wang +11
The recent explosion of large language models (LLMs), each with its own general or specialized strengths, makes scalable, reliable benchmarking more urgent than ever. Standard prac…
Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts
Leiyu Pan, Zhenpeng Su, Minxuan Lv +10
Large language models have demonstrated exceptional performance across a wide range of tasks. However, dense models usually suffer from sparse activation, where many activation val…
Cross-Modal Consistency in Multimodal Large Language Models
Xiang Zhang, Senyu Li, Ning Shi +5
Recent developments in multimodal methodologies have marked the beginning of an exciting era for models adept at processing diverse data types, encompassing text, audio, and visual…