The technology has resulted in a host of cutting-edge AI applications — but its real power lies beyond text generation
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Hello fellow NLP enthusiasts! As the race towards finding better and better neural networks for language modeling continues, I thought it might be a good time to get an overview of the progress made over the years. Enjoy! 😄 Disclaimer: This article is not a complete list of research done in NLP, which would struggle to fit even in several books! Rather, it is a personal overview of some of the mo
Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scala
"> ← Curriculum For Reinforcement Learning Exploration Strategies In Deep Reinforcement Learning →
Date: January 27, 2023 | Estimated Reading Time: 46 min | Author: Lilian Weng Many new Transformer architecture improvements have been proposed since my last post on “The Transformer Family” about three years ago. Here I did a big refactoring and enrichment of that 2020 post — restructure the hierarchy of sections and improve many sections with more recent papers. Version 2.0 is a superset of the
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