Item Discrimination (ID) theory, which is widely used in educational assessment, measures the ability of individual test items to differentiate between high and low performers. Inspired by this theory, we propose an ID-induced prompt synthesis framework for evaluating LLMs to ensure the evaluation set can continually update and refine according to model abilities.

While recent research suggests that current large Vision-Language Models (VLMs) exhibit more reliance on shape, we find them to still be seriously limited in this regard.

These methodologies typically lead to performance bottlenecks and unsatisfactory generalizability.

However, prior work has shown that the data can actually be recovered by the server using so-called gradient inversion attacks.

Quantization of large language models (LLMs) faces significant challenges, particularly due to the presence of outlier activations that impede efficient low-bit representation.

In this paper, we address this gap by dividing a typical patient's clinical journey