Honey, I Shrunk the Language: Language Model Behavior at Reduced Scale.

Vijeta Deshpande, Dan Pechi, Shree Thatte, Vladislav Lialin, Anna Rumshisky

Findings: Large Language Models Findings Paper

Session 7: Large Language Models (Virtual Poster)
Conference Room: Pier 7&8
Conference Time: July 12, 11:00-12:30 (EDT) (America/Toronto)
Global Time: July 12, Session 7 (15:00-16:30 UTC)
Spotlight Session: Spotlight - Metropolitan Centre (Spotlight)
Conference Room: Metropolitan Centre
Conference Time: July 10, 19:00-21:00 (EDT) (America/Toronto)
Global Time: July 10, Spotlight Session (23:00-01:00 UTC)
Keywords: pre-training, scaling, fine-tuning
TLDR: In recent years, language models have drastically grown in size, and the abilities of these models have been shown to improve with scale. The majority of recent scaling laws studies focused on high-compute high-parameter count settings, leaving the question of when these abilities begin to emerge la...
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Abstract: In recent years, language models have drastically grown in size, and the abilities of these models have been shown to improve with scale. The majority of recent scaling laws studies focused on high-compute high-parameter count settings, leaving the question of when these abilities begin to emerge largely unanswered. In this paper, we investigate whether the effects of pre-training can be observed when the problem size is reduced, modeling a smaller, reduced-vocabulary language. We show the benefits of pre-training with masked language modeling (MLM) objective in models as small as 1.25M parameters, and establish a strong correlation between pre-training perplexity and downstream performance (GLUE benchmark). We examine downscaling effects, extending scaling laws to models as small as ~1M parameters. At this scale, we observe a break of the power law for compute-optimal models and show that the MLM loss does not scale smoothly with compute-cost (FLOPs) below $2.2 \times 10^{15}$ FLOPs. We also find that adding layers does not always benefit downstream performance.{Our filtered pre-training data, reduced English vocabulary, and code are available at {https://github.com/text-machine-lab/mini\_bert}{$github.com/text-machine-lab/mini\_bert$}}