Emergence and phase transitions in large language models (LLMs) are phenomena in which new capabilities appear suddenly as models scale in size or complexity, defying expectations based on smaller models. Emergent abilities are capabilities absent in smaller models but manifest in larger ones, often unpredictably. It has been observed that tasks such as arithmetic reasoning, code generation, and multistep logic exhibit abrupt performance improvements at specific model scales, a phenomenon that cannot be predicted by extrapolating from smaller models. Such transitions seem analogous to phase transitions in physicochemical systems. In this talk, we propose a novel mechanism for this dramatic improvement in LLM performance, which occurs through a phase transition in cluster formation as models increase in scale. Using a new framework called statistical teleodynamics, which combines statistical mechanics and potential game theory, we propose a mechanism by which micro-, meso-, and macro-structures emerge in LLMs. This has interesting implications for the design, training, and optimization of LLMs.
