Common Arguments Regarding Emergent Abilities in Large Language Models
Published:May 3, 2023 17:36
•1 min read
•Jason Wei
Analysis
This article discusses the concept of emergent abilities in large language models (LLMs), defined as abilities present in large models but not in smaller ones. It addresses arguments that question the significance of emergence, particularly after the release of GPT-4. The author defends the idea of emergence, highlighting that these abilities are difficult to predict from scaling curves, not explicitly programmed, and still not fully understood. The article focuses on the argument that emergence is tied to specific evaluation metrics, like exact match, which may overemphasize the appearance of sudden jumps in performance.
Key Takeaways
- •Emergent abilities are a key characteristic of large language models.
- •The definition of emergence is tied to the scale of the model.
- •The choice of evaluation metric can influence the perception of emergence.
Reference
“Emergent abilities often occur for “hard” evaluation metrics, such as exact match or multiple-choice accuracy, which don’t award credit for partially correct answers.”