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Summary of Emergent Abilities in Reduced-scale Generative Language Models, by Sherin Muckatira et al.


Emergent Abilities in Reduced-Scale Generative Language Models

by Sherin Muckatira, Vijeta Deshpande, Vladislav Lialin, Anna Rumshisky

First submitted to arxiv on: 2 Apr 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The study investigates whether large language models’ ability to solve new tasks without fine-tuning is solely dependent on their size or if smaller models trained on reduced-scale data can also exhibit this emergent property. To explore this, the researchers simplify pre-training data and train 36 causal language models with varying parameter sizes from 1 million to 165 million. The results show that these smaller models demonstrate enhanced zero-shot capabilities across various tasks in simplified language, achieving performance comparable to larger models on unrestricted language. This suggests that downscaling the language allows zero-shot learning capabilities to emerge in limited-size models.
Low GrooveSquid.com (original content) Low Difficulty Summary
This study shows that large language models can solve new tasks without fine-tuning and investigates if this ability is tied to model size or if smaller models trained on reduced-scale data can also exhibit it. The researchers simplify pre-training data and train 36 causal language models with varying parameter sizes. They find that these smaller models demonstrate enhanced zero-shot capabilities across various tasks in simplified language, achieving performance comparable to larger models.

Keywords

» Artificial intelligence  » Fine tuning  » Zero shot