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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| 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
