The rapid advancement of large language models (LLMs) has reinvigorated the scaling hypothesis of intelligence, suggesting that cognitive capabilities emerge primarily as a function of computational scale. Empirical scaling laws, most notably documented by Kaplan et al. (2020), demonstrate consistent power-law relationships between model size, training compute, and performance across multiple orders of magnitude. Recent work with models like PaLM-2 and GPT-4 appears to support this relationship, showing improvements in tasks ranging from mathematical reasoning to few-shot learning.
However, the emergence debate in AI remains contentious. Critics like Marcus (2023) argue that apparent emergent behaviours in LLMs may instead reflect sophisticated pattern matching operating on increasingly comprehensive training distributions. The observation that capability improvements often track with training volume as much as model size suggests that “emergence” might be better understood as increasingly sophisticated interpolation within a learned manifold rather than true extrapolative reasoning.
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