Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization
Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-domain interactions. Existing adaptation methods struggle to calibrate update magnitude under sparse evidence and thus overfit, whereas history-transfer methods often entangle user preferences with source-domain artifacts, yielding unreliable personalization priors and negative transfer. To calibrate adaptation to evidence quality, we propose PAC-Bayes-regularized Meta-LoRA, 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 predicti...
| Authors | Xuefei Wang, Jun Han, Zixuan Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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