Distinct mechanisms underlying in-context learning in transformers

Researchers have provided a detailed mechanistic analysis of how transformers achieve in-context learning by identifying four algorithmic phases based on memorization, generalization, and the use of 1-point or 2-point statistics. This understanding is crucial for developers as it reveals the underlying mechanisms that enable transformers to adapt their computation to different input data, offering insights into optimizing network performance and training dynamics.

Read the full article at arXiv cs.LG (ML)

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