In plain words: A review of what it costs to train large language models and which choices drive the bill, aimed at people planning experiments or trying to understand the economics. It breaks one intimidating price tag into the parts that matter for budgeting.
Abstract
We review the cost of training large-scale language models, and the drivers of these costs. The intended audience includes engineers and scientists budgeting their model-training experiments, as well as non-practitioners trying to make sense of the economics of modern-day Natural Language Processing (NLP).
Or Sharir, Barak Peleg, Yoav Shoham
arXiv:2004.08900 · cs.CL, cs.LG, cs.NE · submitted Apr 19, 2020
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