Emergent Abilities in Reduced-Scale Generative Language Models
Sherin Muckatira Vijeta Deshpande Vladislav Lialin Anna Rumshisky
Large language models can solve new tasks without task-specific fine-tuning.
This ability, also known as in-context learning (ICL), is considered an
emergent ability and is primarily seen in large language models with billions
of parameters. This study investigates if such emergent properties are strictly
tied to model size or can be demonstrated by smaller models trained on
reduced-scale data. To explore this, we simplify pre-training data and
pre-train 36 causal language models with parameters varying from 1 million to
165 million parameters. We show that models trained on this simplified
pre-training data demonstrate enhanced zero-shot capabilities across various
tasks in simplified language, achieving performance comparable to that of
pre-trained models six times larger on unrestricted language. This suggests
that downscaling the language allows zero-shot learning capabilities to emerge
in models with limited size. Additionally, we find that these smaller models
pre-trained on simplified data demonstrate a power law relationship between the
evaluation loss and the three scaling factors: compute, dataset size, and model
size.