When fine-tuning LLMs with differential privacy, untying input/output embeddings outperforms the standard weight-tied design and enables 60% memory savings—suggesting privacy-preserving training requires rethinking standard model architectures.
This paper investigates weight tying (sharing parameters between input and output embeddings) in large language models trained with differential privacy. The authors find that untying embeddings actually improves performance under DP-SGD, achieving up to 4.74% accuracy gains, while also enabling more memory-efficient privacy techniques.