Previous Lemmy.ml post: https://lemmy.ml/post/1015476 Original X post (at Nitter): https://nitter.net/xwang_lk/status/1734356472606130646
Previous Lemmy.ml post: https://lemmy.ml/post/1015476 Original X post (at Nitter): https://nitter.net/xwang_lk/status/1734356472606130646
It’s not the definition in the paper. Here is the context:
What this means is, that we cannot, for example, predict chemistry from physics. Physics studies how atoms interact, which yields important insights for chemistry, but physics cannot be used to predict, say, the table of elements. Each level has its own laws, which must be derived empirically.
LLMs obviously show emergence. Knowing the mathematical, technological, and algorithmic foundation, tells you little about how to use (prompt, train, …) an AI model. Just like knowing cell biology will not help you interact with people, even if they are only colonies of cells working together.
The paper talks specifically about “emergent abilities of LLMs”:
The authors further clarify:
Bigger models perform better. An increase in the number of parameters correlates to an increase in the performance on tests. It had been alleged, that some abilities appear suddenly, for no apparent reason. These “emergent abilities of LLMs” are a very specific kind of emergence.