1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2025
EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models
Xingrun Xing, Zheng Liu, Shitao Xiao +6
Modern large language models (LLMs) driven by scaling laws, achieve intelligence emergency in large model sizes. Recently, the increasing concerns about cloud costs, latency, and p…
cs.LG2025
Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation
Boyan Gao, Bo Zhao, Shreyank N Gowda +4
Dataset condensation aims to synthesize datasets with a few representative samples that can effectively represent the original datasets. This enables efficient training and produce…
cs.CL2024★ 1 cited
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
Yiming Ju, Ziyi Ni, Xingrun Xing +4
Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks. In this work, we demonstrate that the order of training data can lead to signif…