Despite the remarkable progress, they are vulnerable to deliberate attacks, giving rise to concerns about the reliability and trustworthiness of these models in real-world scenarios.

Extensive experiments on benchmarks from different DG tasks demonstrate that LFME is consistently beneficial to the baseline and can achieve comparable performance to existing arts.

First, we define distributionally equivalent and Max-Information model extraction attacks, and reduce them into a variational optimisation problem.

It's important to note that

While existing defenses often rely on regularization techniques to reduce information leakage, they remain vulnerable to recent attacks.

Among all the contributing factors, the quality and selection of data is becoming increasingly recognized for its importance in training LLMs effectively.