Attention-based neural networks such as transformers have demonstrated a remarkable
ability to exhibit in-context learning (ICL): Given a short prompt sequence of tokens from an …

In order to understand the in-context learning phenomenon, recent works have adopted a
stylized experimental framework and demonstrated that Transformers can learn gradient …

Going beyond mimicking limited human experiences, recent studies show initial evidence
that, like humans, large language models (LLMs) are capable of improving their abilities …

While most ML models expect independent and identically distributed data, this assumption
is often violated in real-world scenarios due to distribution shifts, resulting in the degradation …

Autoregressively trained transformers have brought a profound revolution to the world,
especially with their in-context learning (ICL) ability to address downstream tasks. Recently …

In machine learning, generalization against distribution shifts--where deployment conditions
diverge from the training scenarios--is crucial, particularly in fields like climate modeling …

Large language models (LLMs) like transformers have impressive in-context learning (ICL)
capabilities; they can generate predictions for new queries based on input-output …

Large language models (LLMs) like transformers demonstrate impressive in-context
learning (ICL) capabilities, allowing them to make predictions for new tasks based on prompt …

Large language models exhibit a remarkable capacity for in-context learning, where they
learn to solve tasks given a few examples. Recent work has shown that transformers can be …

Transformers have demonstrated remarkable in-context learning (ICL) capabilities, adapting
to new tasks by simply conditioning on demonstrations without parameter updates …