Ashish Vaswani et. al. introduce Transformers and Attention in this classic 2017 paper.
The Transformer architecture relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely for sequence transduction tasks. This new network design improves translation quality while being more parallelizable and significantly faster to train than previous models.
- Achieved 28.4 BLEU on the WMT 2014 English-to-German translation task.
- Reached a state-of-the-art score of 41.8 BLEU for English-to-French using eight GPUs in only 3.5 days.
- Demonstrates successful application to English constituency parsing with both large and limited training data sets.
The Transformer architecture relies solely on attention mechanisms, dispensing with recurrence and convolutions entirely for sequence transduction tasks. This new network design improves translation quality while being more parallelizable and significantly faster to train than previous models.
- Achieved 28.4 BLEU on the WMT 2014 English-to-German translation task.
- Reached a state-of-the-art score of 41.8 BLEU for English-to-French using eight GPUs in only 3.5 days.
- Demonstrates successful application to English constituency parsing with both large and limited training data sets.
The attention mechanism in Large Language Models (LLMs) helps derive the meaning of a word from its context. This involves encoding words as multi-dimensional vectors, calculating query and key vectors, and using attention weights to adjust the embedding based on contextual relevance.
The article delves into how large language models (LLMs) store facts, focusing on the role of multi-layer perceptrons (MLPs) in this process. It explains the mechanics of MLPs, including matrix multiplication, bias addition, and the Rectified Linear Unit (ReLU) function, using the example of encoding the fact that Michael Jordan plays basketball. The article also discusses the concept of superposition, which allows models to store a vast number of features by utilizing nearly perpendicular directions in high-dimensional spaces.
