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Transformers as Cross-Task Learners: Shared Structure Drives Sample Efficiency in In-Context Learning
Transformers achieve remarkable performance by jointly learning broad families of tasks during pretraining and adapting to unseen tasks from only a short prompt. Yet a rigorous mathematical and statistical understanding of this phenomenon remains limited. This paper aims to study how Transformers ex
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