5 citations · 12 across the 4 of their papers we have counts for
4 papers
Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts
Jialin Wu, Xia Hu, Yaqing Wang +2
Large multi-modal models (LMMs) exhibit remarkable performance across numerous tasks. However, generalist LMMs often suffer from performance degradation when tuned over a large col…
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling
Yaqing Wang, Jialin Wu, Tanmaya Dabral +8
Large language models (LLMs) and vision language models (VLMs) demonstrate excellent performance on a wide range of tasks by scaling up parameter counts from O(10^9) to O(10^{12})…
Automated Evaluation of Personalized Text Generation using Large Language Models
Yaqing Wang, Jiepu Jiang, Mingyang Zhang +4
Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context. While the research progress in this area has bee…
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
Lu Yin, You Wu, Zhenyu Zhang +10
Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal mode…