Papers by Lichun Li
Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) methods have gained popularity for adapting pre-trained Large Language Models (LLMs) to downstream tasks. |
| Approach: | They propose a method to optimize the importance of full layers with layer-wise importance scoring by leveraging the estimated importance scores. |
| Outcome: | The proposed method is compatible with PEFT methods that operate on a per-layer basis and achieves better performance. |
GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models (2025.acl-long)
Copied to clipboard
Kai Yao, Zhaorui Tan, Penglei Gao, Lichun Li, Kaixin Wu, Yinggui Wang, Yuan Zhao, Yixin Ji, Jianke Zhu, Wei Wang
| Challenge: | Existing methods for offsite-tuning of large language models require high computational costs and lack theoretical analysis. |
| Approach: | They propose an offsite-tuning approach that selectively applies compression techniques such as rank compression and channel pruning to preserve the gradients of fine-tuned adapters while ensuring privacy. |
| Outcome: | The proposed method surpasses existing OT methods in privacy protection and model performance. |