1 citations · 1 across the 4 of their papers we have counts for
4 papers
COEC: Calibrated Orthogonal-Equivalence Compensation for Structured Pruning of Large Language Models
Peiqi Yu, Nam Ling, Wei Wang +1
Structured pruning reduces the size and inference cost of large language models (LLMs) by removing weight columns, but the resulting output error can degrade accuracy. Existing tra…
Efficient Post-Training Pruning of Large Language Models with Statistical Correction
Peiqi Yu, Jinhao Wang, Xinyi Sui +3
Post-training pruning is an effective approach for reducing the size and inference cost of large language models (LLMs), but existing methods often face a trade-off between pruning…
Prompt-Guided Internal States for Hallucination Detection of Large Language Models
Fujie Zhang, Peiqi Yu, Biao Yi +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of tasks in different domains. However, they sometimes generate responses that are logically…
Nonconvex models for recovering images corrupted by salt-and-pepper noise on surfaces
Yuan Liu, Peiqi Yu, Chao Zeng
Image processing on surfaces has drawn significant interest in recent years, particularly in the context of denoising. Salt-and-pepper noise is a special type of noise which random…