Large language models (LLMs) with billions of parameters demonstrate impressive performance.

Therefore, for better cross-task generalization, the "zero-shot" learning ability of LLMs is crucial for

In particular, prior works often generate pseudo-label for unlabeled images and then train the model on those samples.

Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities.