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cs.AI2024
Efficient Dynamic Ensembling for Multiple LLM Experts
Jinwu Hu, Yufeng Wang, Shuhai Zhang +5
LLMs have demonstrated impressive performance across various language tasks. However, the strengths of LLMs can vary due to different architectures, model sizes, areas of training…
cs.LG2024★ 3 cited
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen +2
Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, in real-world scenarios, models are…
cs.LG2024
Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting
Mingkui Tan, Guohao Chen, Jiaxiang Wu +4
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample. Although recent TTA has sh…