Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization
New Meta-LoRA framework tackles overfitting and negative transfer to boost zero-shot personalization performance.
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, ...
ArXiv CS.AI
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This research solves a critical bottleneck in LLM personalization by balancing the need for rapid adaptation with the risk of overfitting on sparse data. By using Meta-LoRA to dynamically calibrate update strength based on evidence quality, the model achieves more reliable and stable cross-domain performance.
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