The discussion reveals the dual risks of embedding data leakage, highlighting that both inversion attacks and model outputs can inadvertently expose sensitive information. Patricia explains how embeddings, which can represent more than just words, may leak contextually relevant data, even when not explicitly queried. The conversation emphasizes the need to differentiate between direct access to embeddings and the risks associated with model outputs.
