Scaling Transformer to 1M Tokens and Beyond with RMT - Paper Explained

Explore a detailed analysis of the Recurrent Memory Transformer (RMT) technique, which promises to scale transformers to 1 million tokens and beyond. Learn about the strengths and weaknesses of this approach, its application to extend BERT's context length, and its potential impact on long-term dependency handling in natural language processing. Dive into the paper's key concepts, including the storage and processing of local and global information, information flow between input sequence segments, and experiments demonstrating the effectiveness of RMT. Gain insights into the unprecedented two-million-token context length achievement and its implications for memory-intensive applications in AI and language understanding.

- Intro
- Transformers on long sequences
- Tasks considered
- Recurrent Memory Transformer
- Experiments on scaling and attention maps
- Conclusion