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cs.CR2024★ 2 cited
Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization
Tiantian Liu, Hongwei Yao, Feng Lin +3
Embeddings have become a cornerstone in the functionality of large language models (LLMs) due to their ability to transform text data into rich, dense numerical representations tha…
cs.CR2024★ 2 cited
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning
Binhao Ma, Tianhang Zheng, Hongsheng Hu +5
Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. However, this utility comes with…