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Despite a surge of empirical interest in the ``unreasonable effectiveness''of drastic data pruning for AI systems, a unified theoretical understanding of this phenomenon remains elusive. In this work, we provide a precise high-dimensional theory of learning under data pruning via weighted empirical risk minimization (E...
This work study capacity-limited scaling in a bottlenecked bigram model with power-law (Zipf) token statistics, viewed as an analytically tractable toy model of a large language model, and suggests that heavy-tailed language statistics may underlie both optimization- and capacity-driven scaling.
Self-attention has emerged as a fundamental component driving the success of modern transformer architectures which power large language models (ChatGPT, Llama, etc.) and various other types of systems. However, a theoretical understanding of how such models actually work is still under active development. The recent w...
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