Predicting Emergent Abilities in Large Language Models through Infinite Resolution Evaluation

Large language models exhibit emergent abilities that are difficult to predict as they scale up in size. This study introduces an evaluation strategy called PASSUNTIL that enables quantitative exploration of the scaling properties of task performance, leading to the discovery of a strict task scaling law and an accelerated emergence phenomenon.