3 papers
cs.LG2026
CoSA: Compressed Sensing-Based Adaptation of Large Language Models
Songtao Wei, Yi Li, Bohan Zhang +6
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical paradigm for adapting large language models (LLMs) without updating all parameters. Most existing approaches, such…
cs.CL2026
Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?
Xu Hu, Yifan Zhang, Songtao Wei +4
Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assumed to improve factual correctness. Howev…
cs.CV2025
AdaCM: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction
Yuanbin Man, Ying Huang, Chengming Zhang +3
The advancements in large language models (LLMs) have propelled the improvement of video understanding tasks by incorporating LLMs with visual models. However, most existing LLM-ba…