4 papers
Towards Efficient LLMs Annealing with Principled Sample Selection
Yuanjian Xu, Jianing Hao, Wanbo Zhang +2
The annealing phase is a pivotal convergence stage in LLM pre-training that ultimately determines final model quality. However, effectively selecting training data during this phas…
D: Dynamic Directional Graph-Constrained Data Scheduling for LLM Training
Yuanjian Xu, Jianing Hao, Guang Zhang +1
Training data plays a central role in large language models (LLMs) optimization, motivating extensive research on data scheduling strategies. Most existing approaches concentrate o…
Quantum-Inspired Fine-Tuning for Few-Shot AIGC Detection via Phase-Structured Reparameterization
Kaiyang Xing, Han Fang, Zhaoyun Chen +4
Recent studies show that quantum neural networks (QNNs) generalize well in few-shot regimes. To extend this advantage to large-scale tasks, we propose Q-LoRA, a quantum-enhanced fi…
Data-Efficient Training by Evolved Sampling
Ziheng Cheng, Zhong Li, Jiang Bian
Data selection is designed to accelerate learning with preserved performance. To achieve this, a fundamental thought is to identify informative data samples with significant contri…