2 citations · 2 across the 4 of their papers we have counts for
4 papers
Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models
Dayan Pan, Jingyuan Wang, Xie Yu
Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks. However, most existing PEFT methods rely on uniform an…
RoSA: Enhancing Parameter-Efficient Fine-Tuning via RoPE-aware Selective Adaptation in Large Language Models
Dayan Pan, Jingyuan Wang, Yilong Zhou +3
Fine-tuning large language models is essential for task-specific adaptation, yet it remains computationally prohibitive. Parameter-Efficient Fine-Tuning (PEFT) methods have emerged…
Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models
Dayan Pan, Zhaoyang Fu, Jingyuan Wang +3
Large Language Models (LLMs) possess remarkable generalization capabilities but struggle with multi-task adaptation, particularly in balancing knowledge retention with task-specifi…
A Dataset for Spatiotemporal-Sensitive POI Question Answering
Xiao Han, Dayan Pan, Xiangyu Zhao +4
Spatiotemporal relationships are critical in data science, as many prediction and reasoning tasks require analysis across both spatial and temporal dimensions--for instance, naviga…