3 papers
cs.LG2026
Bayesian Model Merging
Kaiyang Li, Shaobo Han, Qing Su +1
Model merging aims to combine multiple task-specific expert models into a single model without joint retraining, offering a practical alternative to multi-task learning when data a…
cs.LG2025
Uni-LoRA: One Vector is All You Need
Kaiyang Li, Shaobo Han, Qing Su +3
Low-Rank Adaptation (LoRA) has become the de facto parameter-efficient fine-tuning (PEFT) method for large language models (LLMs) by constraining weight updates to low-rank matrice…
cs.CL2024
VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks
Yang Li, Shaobo Han, Shihao Ji
As the adoption of large language models increases and the need for per-user or per-task model customization grows, the parameter-efficient fine-tuning (PEFT) methods, such as low-…