Data Flow Risks
Data flows in large language models pose significant risks, especially when sending information externally. It's crucial to practice data minimization to reduce the chance of breaches, as even internal models can inadvertently memorize and leak sensitive data. Additionally, the misconception that embeddings are safe can lead to unintended exposure of personal information, highlighting the need for careful handling of contextual data.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Ensuring Privacy for Any LLM with Patricia Thaine - 716
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