"Those relationships map out context, and context builds meaning in language. For example, in the sentence "The bank raised interest rates," attention helps the model establish that "bank" relates to "interest rates" in a financial context, not a riverbank context. Through attention, conceptual relationships become quantified as numbers stored in a neural network. Attention also governs how AI language models choose what information "matters most" when generating each word of their response."
"Even so, the original Transformer architecture from 2017 checked the relationship of each word in a prompt with every other word in a kind of brute force way. So if you fed 1,000 words of a prompt into the AI model, it resulted in 1,000 x 1,000 comparisons, or 1 million relationships to compute. With 10,000 words, that becomes 100 million relationships. The cost grows quadratically, which created a fundamental bottleneck for processing long conversations."
Attention quantifies how words in text relate to one another, mapping context that builds meaning and guiding which information matters during generation. The Transformer architecture computes pairwise relationships between tokens, producing quadratic growth in comparisons as sequence length increases. GPUs and parallel computation made attention practical at moderate lengths, but brute-force pairwise checks become costly for thousands or tens of thousands of tokens. That quadratic cost creates a fundamental bottleneck for long conversations and long-context uses. Sparse attention techniques can reduce cost, but reprocessing entire conversation history for each response maintains performance penalties in long interactions.
Read at Ars Technica
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