Emergent Abilities of Large Language Models

作者：Wei, Jason, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H., Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus · 发表于：arXiv (Cornell University) · 年份：2022 · DOI：10.48550/arxiv.2206.07682 · 被引用次数：1042 · 研究领域：Topic Modeling、Machine Learning and Data Classification、Machine Learning and Algorithms

Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictable phenomenon that we refer to as emergent abilities of large language models. We consider an ability to be emergent if it is not present in smaller models but is present in larger models. Thus, emergent abilities cannot be predicted simply by extrapolating the performance of smaller models. The existence of such emergence implies that additional scaling could further expand the range of capabilities of language models.