4 papers · 1 filter
Federated Large Language Models: Current Progress and Future Directions
Yuhang Yao, Jianyi Zhang, Junda Wu +11
Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy…
Group Distributionally Robust Dataset Distillation with Risk Minimization
Saeed Vahidian, Mingyu Wang, Jianyang Gu +3
Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic dataset that captures the essential information of a training dataset, facilitating the…
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
Qinsi Wang, Saeed Vahidian, Hancheng Ye +3
Large language models (LLMs) with billions of parameters have sparked a new wave of exciting AI applications. However, their high computational costs and memory demands during infe…
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning
Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu +4
Dataset distillation (DD) is an increasingly important technique that focuses on constructing a synthetic dataset capable of capturing the core information in training data to achi…