In the field of natural language processing, the prevalent approach involves fine-tuning pretrained language models (PLMs) using local samples.

To overcome these challenges, we propose a G AN-based LA tent S pace S earch attack ( GLASS) that harnesses abundant prior knowledge from public data using advanced StyleGAN technologies. Additionally, we introduce GLASS++ to enhance reconstruction stability.

In the United States, health insurance is a critical component of individuals' ability to access health care.

This paper reveals that the common downstream practice of taking a dot product is only a zeroth-order approximation of the optimization goal, resulting in a loss of information during test-time.