3 papers
cs.CL2026
ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation
Bo Xu, Haotian Wu, Hehai Lin +4
Model merging aims to combine multiple task-specific expert models into a single model while preserving generalization across diverse tasks. However, interference among experts, es…
cs.LG2026
Boosting Meta-Learning for Few-Shot Text Classification via Label-guided Distance Scaling
Yunlong Gao, Xinyue Liu, Yingbo Wang +2
Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithm…
cs.CV2024
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks
Xinyue Liu, Yunlong Gao, Linlin Zong +1
Meta-learning has emerged as a prominent technology for few-shot text classification and has achieved promising performance. However, existing methods often encounter difficulties…