Amazon new 20B-parameter Alexa model sets new marks in few-shot learning

Good news! Amazon is again pushing the boundaries of computational linguistics! 

This seems to be very interesting work. I have not had a chance to read the related research paper.

"As part of this move, we have introduced Transformer-based large-scale multilingual language models we call Alexa Teacher Models (AlexaTM). Given only a few examples of a task in a new language, AlexaTM can transfer what it knows to the new language with no extra human supervision.

In a paper we’re presenting at this year’s Knowledge Discovery and Data Mining Conference (KDD), we showed that 10-billion- and two-billion-parameter AlexaTM models can improve on state-of-art cross-lingual transfer learning and increase Alexa’s accuracy in different locales.

In a follow-up paper, which we've published on arXiv, we have taken this line of research a step further, with a 20-billion-parameter generative model called AlexaTM 20B. The experiments reported in the paper — which use only publicly available data — show that AlexaTM 20B can not only transfer what it learns across languages but also learn new tasks from just a handful of examples (few-shot learning). ...
he gains in translating to and from low-resource languages like Marathi, Tamil, and Telugu are particularly significant ..."

20B-parameter Alexa model sets new marks in few-shot learning - Amazon Science With an encoder-decoder architecture — rather than decoder only — the Alexa Teacher Model excels other large language models on few-shot tasks such as summarization and machine translation.