HomeResearch LibraryEmergent Abilities of Large Language Models
empirical studyChapter 7TMLR · 2022

Emergent Abilities of Large Language Models

Jason Wei (Google Brain), Yi Tay (Google Brain)

Abstract

We discuss emergent abilities of large language models — abilities that are not present in smaller models but appear in larger models. We survey 137 emergent abilities across 8 models.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Emergent abilities are both the promise and the peril of Zone III. The promise: capabilities that enable autonomous enterprise workflows emerge at scale. The peril: emergent behaviors are unpredictable and may include undesired capabilities that complicate governance.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Emergent ability taxonomy
  • →Scaling-capability relationship
  • →137 emergent abilities documented

Topics

emergent abilitiesscalingLLM capabilitiesphase transitions