Emergent Behaviors in LLMs: A Plausibility Argument
2025-05-08
Large Language Models (LLMs) exhibit surprising emergent behaviors: a sudden ability to perform new tasks when the parameter count reaches a certain threshold. This article argues that this isn't coincidental, exploring potential mechanisms through examples from nature, machine learning algorithms, and LLMs themselves. The author posits that LLM training is like searching for an optimal solution in high-dimensional space; sufficient parameters allow coverage of the algorithm space needed for specific tasks, unlocking new capabilities. While predicting when an LLM will acquire a new capability remains challenging, this research offers insights into the underlying dynamics of LLM improvement.