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Unpacks how large language models acquire unexpected, new capabilities (emergent abilities) when scaled beyond certain thresholds.

Overview

This seminal paper introduces and explores the phenomenon of "emergent abilities" in large language models (LLMs), where new capabilities arise unpredictably when models are scaled to a certain size. It details various examples across different tasks, demonstrating that these abilities are not present in smaller models but appear suddenly as scale increases. The findings have profound implications for understanding and developing advanced AI systems, suggesting that simply scaling models can unlock unforeseen intelligence. It highlights the importance of continued research into the mechanisms behind these emergent behaviors.

Abstract

Large language models (LLMs) display emergent abilities, which are abilities not present in smaller models but arise in larger models. Emergent abilities cannot be predicted from the performance of smaller models, hence it is not known whether or when capabilities like question answering, summarization, or dialog will emerge as models scale. We provide an overview of the current understanding of emergent abilities, discuss their potential causes, and implications for designing and interpreting experiments with LLMs. We also provide a list of emergent abilities and the models/datasets on which they were observed.