2022-06-15 · preprint · Topic Modeling · en
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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.
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The work has received 1,263 citations, a count that marks it as an established reference point in its field. It references 0 works in the available metadata.
3 cited facts
Emergent Abilities of Large Language Models is a preprint, focusing on Topic Modeling, published in 2022.
Emergent Abilities of Large Language Models has 1,263 citations in our record, placing it among the highly cited works we track.
Our record lists 3 authors for Emergent Abilities of Large Language Models. Author names aren't in our data yet — the source link on the page lists the full byline.
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