AI RESEARCH
Re-examining Low Rank adaptation for private LLM fine-tuning
arXiv CS.LG
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ArXi:2510.01137v3 Announce Type: replace Privacy is a central concern when fine-tuning large language models (LLMs) on sensitive data, and differentially private stochastic gradient descent (DP-SGD) -- which clips per-sample gradients and adds calibrated Gaussian noise -- is the standard tool for formal privacy guarantees. Both theory and practice show that lower-rank models are better suited to