Live Breaking News & Updates on Sample Efficient Text Summarization Using

Discussions:
Hacker News (64 points, 3 comments), Reddit r/MachineLearning (219 points, 18 comments)


Translations: Simplified Chinese, French, Korean, Russian, Turkish






This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn’t a particularly novel architecture – it’s architecture is very similar to the decoder-only transformer. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we’ll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. And then we’ll look at applications for the decoder-only transformer beyond language modeling.

My goal here is to also supplement my earlier post, The Illustrated Transformer, ....

Mohammad Saleh , Ryan Sepassi , Lukasz Kaiser , Peterj Liu , Neural Network , Hacker News , Simplified Chinese , Illustrated Transformer , Brain Surgery , Looking Inside , Language Modeling , Illustrated Word , Generating Wikipedia , Summarizing Long Sequences , Character Level Language Modeling , Deeper Self Attention , First Law , Byte Pair Encoding , Illustrated Self Attention , Processing One Token , Connected Neural Network , Beyond Language Modeling , Sample Efficient Text Summarization Using , Single Pre Trained Transformer , Music Transformer , Hugging Face ,