4 papers · 1 filter
Advanced Black-Box Tuning of Large Language Models with Limited API Calls
Zhikang Xie, Weilin Wan, Peizhu Gong +2
Black-box tuning is an emerging paradigm for adapting large language models (LLMs) to better achieve desired behaviors, particularly when direct access to model parameters is unava…
Explore and Establish Synergistic Effects Between Weight Pruning and Coreset Selection in Neural Network Training
Weilin Wan, Fan Yi, Weizhong Zhang +2
Modern deep neural networks rely heavily on massive model weights and training samples, incurring substantial computational costs. Weight pruning and coreset selection are two emer…
Computational Budget Should Be Considered in Data Selection
Weilin Wan, Weizhong Zhang, Cheng Jin
Data selection improves computational efficiency by choosing informative subsets of training samples. However, existing methods ignore the compute budget, treating data selection a…
Towards Robust Influence Functions with Flat Validation Minima
Xichen Ye, Yifan Wu, Weizhong Zhang +2
The Influence Function (IF) is a widely used technique for assessing the impact of individual training samples on model predictions. However, existing IF methods often fail to prov…