Understanding Nuanced Relationships: A Look at Transformer Architectures
- The Transformer architecture was introduced in 2017
- It uses self-attention to address the limitations of recurrent neural networks, such as difficulty in parallelization and the vanishing and exploding gradient problem
- Self-attention allows the model to weigh importance of different parts of the input without maintaining an internal State
- This is done by three matrices (Q, K, V) whose weights are learned with back propagation
- The result is that Transformers can focus on specific parts of sentences and understand nuanced relationships between words.
Exploring Transformer Models to Enhance Language Processing Performance
- Transformer models are used for language processing tasks such as translation, summarization and creative tasks
- Attention is used to dynamically weight the contribution of different input sequence elements in output
- The attention mechanism uses three matrices and values obtained from back propagation over many training examples
- These attention and encoding mechanisms enable impressive model performance.