Attention is a neural-network mechanism that weighs relationships between different parts of input data. Transformer-based models use it to capture global context, making it useful for text generation and other large-scale sequence tasks. section 1.4.1 section 1.5
Self-attention changes how a model interprets a sequence:
- It evaluates the relative importance of words instead of treating them in isolation. section 1.5
- It considers words before and after a term, helping determine meaning from full context. section 1.5
- It lets transformers process input data in parallel while capturing long-range dependencies. section 1.5
- It supports applications where relationships across a sequence matter, including language translation, code, music, and time-series prediction. section 1.4.1
Resolving a distant reference
In “The lion, which had been chasing the antelope, finally caught it,” self-attention helps connect “it” with “the antelope,” despite the words between them. This broader context supports more coherent language understanding and generation. section 1.5
Attention contributes to the efficiency and accuracy of transformer-based language modeling, but architecture is only one production consideration. Data quality, output reliability, resource demands, security, bias, and regulatory compliance also affect system choices. section 1.7 section 1.7
