Transformers learn to approximate second-order optimization methods for ICL.

Learning the intents of an agent, defined by its goals or motion style, is often extremely challenging from just a few examples.

Machine unlearning is a promising solution for efficiently removing specific knowledge by post hoc modifying models.

Evaluating large language models (LLMs) is challenging.