Emergent Abilities of Large Language Models

Editorial record

Plain-language summary

The authors survey tasks where accuracy stays near chance for small models and then rises sharply beyond a certain parameter or compute scale, calling these emergent abilities. They catalog examples across few-shot prompting and augmented-prompting settings, showing the jumps are not evident from the performance trend of smaller models. The claim shaped how the field reasoned about scaling, suggesting some capabilities cannot be forecast and appear only after crossing a size threshold.