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arXiv Machine Learning By Songyuan Sui, Srikanth Malla, Chiho Choi, Joon Hee Choi

Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation

Read the original on arXiv Machine Learning →

The paper investigates how to reduce the per-user adaptation burden in personalized large language models by separating reusable personalization capacity from user-specific adjustments. Through empirical studies, it shows that shared low‑rank factors can capture much of the cross‑user structure, while a tiny user code suffices for individual correction. The proposed LINEUP framework achieves state‑of‑the‑art performance on six personalized tasks while using only eight scalars per user compared to millions of private‑LoRA parameters.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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