A New AI Research Paper From Stanford Presents an Alternative Explanation for Seemingly Sharp

This paper discusses emergent abilities, which are characteristics of large language models (LLM) such as GPT, PaLM, and LaMDA.

These LLM’s have what are known as ergent abilities, and they can be useful for machine learning.

In this paper, the authors explore the idea that these capabilities may arise due to sudden, unanticipated changes in model outputs as a function of model scale on particular tasks.

They suggest that larger models may acquire unwanted mastery over less hazardous skills due to their inherent tendency to fail at certain tasks.

Due to advances in machine learning techniques such as those reported by Stanford, there has been a lot of interest in emergent ability, or, in other words, behavior that is governed by an unchanging underlying principle: size does not always guarantee quality.

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