Qilu Shen

Towards Understanding In-Context Learning of Transformers Under Non-I.I.D. Scenarios

Understanding the generalization behavior of in-context learning (ICL) in Transformers remains a fundamental challenge, as most existing theoretical analyses are based on the assumption that data are independently and identically distributed (i.i.d.), an assumption that often does not hold in practi