Zi-Xuan Wang

Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization

PAC-Bayes-regularized Meta-LoRA is proposed, which uses a meta-learned LoRA initialization as both the adaptation start and prior center, while adjusting update strength according to support-set size and predictive uncertainty to limit overfitting under sparse or ambiguous evidence.