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Is Weight Tying Still Beneficial for Decoder-Only LLMs in Private Settings Under DP-SGD?
Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). Many decoder-only LLMs employ weight tying between input and output embeddings, a design choice originally introduced for parameter efficiency and imp
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