Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering.

The proposed indicators adequately measure the effectiveness of the soft labels generated in this process.

The behavior emerges and becomes more robust as the architecture scales up its number of parameters.

Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.

Technically, our result is enabled by establishing a link with recent works on optimal denoising of extensive-rank matrices and on the ellipsoid fitting problem.

However, current Vid-LLMs struggle to simultaneously retain high-quality frame-level semantic information ( i.e., a sufficient