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most citedGenerated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data

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Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data

Atilla Akkus, Masoud Poorghaffar Aghdam, Mingjie Li +4

Large language models (LLMs) have demonstrated significant success in various domain-specific tasks, with their performance often improving substantially after fine-tuning. However…

cs.CL2024

Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders

Yuan Xin, Zheng Li, Ning Yu +4

Despite being prevalent in the general field of Natural Language Processing (NLP), pre-trained language models inherently carry privacy and copyright concerns due to their nature o…