As large language models gain widespread adoption, running them efficiently becomesacrucialtask.

The moments of a random variable are arguably the most popular all-purpose statistic.

Moreover,VQG models are also particularly useful for the few-shot learning or zero-shot learning [36,44]. Conceptually, VQG is a very challenging task since the generated questions are not only required to be consistent with the image content but also meaningful and answerabletohumans.

We introduce Lifelong ICL, a problem setting that challenges long-context language models (LMs) to learn a sequence of language tasks through in-context learning(ICL).