Transformers and attention mechanisms
Attention mechanisms in language models such as GPT-4 are a transformative innovation that enables the model to selectively focus on specific parts of the input data, much like how human attention allows us to concentrate on particular aspects of what we’re reading or listening to. Here’s an in-depth explanation of how attention mechanisms function within these models:
- Concept of attention mechanisms: The term “attention” in the context of neural networks draws inspiration from the attentive processes observed in human cognition. The attention mechanism in neural networks was introduced to improve the performance of encoder-decoder architectures, especially in tasks such as machine translation, where the model needs to correlate segments of the input sequence with the output sequence.
- Functionality of attention mechanisms:
- Contextual relevance: Attention mechanisms weigh the elements of the input sequence...
