45 citations · 69 across the 16 of their papers we have counts for
4 papers · 2 filters
Parameter-Efficient Fine-Tuning via Circular Convolution
Aochuan Chen, Jiashun Cheng, Zijing Liu +4
Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, leveraging low-rank matrices and to represent weight changes (i.…
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
Yuhan Li, Peisong Wang, Xiao Zhu +5
The emergence of large language models (LLMs) has revolutionized the way we interact with graphs, leading to a new paradigm called GraphLLM. Despite the rapid development of GraphL…
Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
Ziqi Gao, Qichao Wang, Aochuan Chen +4
Low-rank adaptation~(LoRA) has recently gained much interest in fine-tuning foundation models. It effectively reduces the number of trainable parameters by incorporating low-rank m…
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining
Haihong Zhao, Aochuan Chen, Xiangguo Sun +2
Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP). One of the most notable advancements of LLMs is that a si…