Microsoft and Nvidia team up to train one of the world’s largest language models

Microsoft and Nvidia today announced that they trained what they claim is the largest and most capable AI-powered language model to date: Megatron-Turing Natural Language Generation (MT-NLP). The successor to the companies’ Turing NLG 17B and Megatron-LM models, MT-NLP contains 530 billion parameters and achieves “unmatched” accuracy in a broad set of natural language tasks, Microsoft and Nvidia say — including reading comprehension, commonsense reasoning, and natural language inferences.

“The quality and results that we have obtained today are a big step forward in the journey towards unlocking the full promise of AI in natural language. The innovations of DeepSpeed and Megatron-LM will benefit existing and future AI model development and make large AI models cheaper and faster to train,” Nvidia’s senior director of product management and marketing for accelerated computing, Paresh Kharya, and group program manager for the Microsoft Turing team, Ali Alvi wrote in a blog post. “We look forward to how MT-NLG will shape tomorrow’s products and motivate the community to push the boundaries of natural language processing (NLP) even further. The journey is long and far from complete, but we are excited by what is possible and what lies ahead.”

Training massive language models

In machine learning, parameters are the part of the model that’s learned from historical training data. Generally speaking, in the language domain, the correlation between the number of parameters and sophistication has held up remarkably well. Language models with large numbers of parameters, more data, and more training time have been shown to acquire a richer, more nuanced understanding of language, for example gaining the ability to summarize books and even complete programming code.

To train MT-NLG, Microsoft and Nvidia say that they created a training dataset with 270 billion tokens from English-language websites. Tokens, a way of separating pieces of text into smaller units in natural language, can either be words, characters, or parts of words. Like all

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