Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
A novel framework for transferring soft prompts between LLMs while preserving privacy through differential privacy and knowledge distillation techniques.
ICML 2025 ,
Publications - Xun Wang
You can also find my articles on my Google Scholar profile.
Structurally Prune Anything: Any Architecture, Any Framework, Any Time
A versatile structured pruning framework that works across any neural network architecture, framework, and training stage, introducing OBSPA for post-training pruning without fine-tuning.
arXiv preprint ,