2 citations · 2 across the 14 of their papers we have counts for
5 papers · 1 filter
NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning
Zhi Zhang, Yixian Shen, Congfeng Cao +1
Existing parameter-efficient fine-tuning (PEFT) methods primarily fall into two categories: addition-based and selective in-situ adaptation. The former, such as LoRA, introduce add…
SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing
Jing Chen, Zhiheng Yang, Yixian Shen +4
Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution…
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection
Yixian Shen, Qi Bi, Jia-Hong Huang +3
We present a new adaptation method MaCP, Minimal yet Mighty adaptive Cosine Projection, that achieves exceptional performance while requiring minimal parameters and memory for fine…
SSH: Sparse Spectrum Adaptation via Discrete Hartley Transformation
Yixian Shen, Qi Bi, Jia-Hong Huang +3
Low-rank adaptation (LoRA) has been demonstrated effective in reducing the trainable parameter number when fine-tuning a large foundation model (LLM). However, it still encounters…
Gradient Weight-normalized Low-rank Projection for Efficient LLM Training
Jia-Hong Huang, Yixian Shen, Hongyi Zhu +2
Large Language Models (LLMs) have shown remarkable performance across various tasks, but the escalating demands on computational resources pose significant challenges, particularly…