Modern AI explanations often celebrate the “attention mechanism”, presenting it as if the model is focusing, like a diligent student scanning a text. The metaphor implies conscious prioritisation, selective awareness, and intent.
Charming — but completely misleading.
The Metaphor Problem
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Attention as focus suggests agency, deliberation, and intention.
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Reality: attention in an LLM is a weighted mapping of correlations between tokens, not a spotlight cast by a sentient mind.
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This framing invites users to imagine that the model “decides what matters,” rather than simply executing relational calculations.
Why This Is Misleading
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Anthropomorphises statistical operations — weights and matrices become volitional acts.
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Obscures relational structure — what we call “focus” is just a mapping of patterns in context.
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Encourages overestimation of understanding — users may assume comprehension where only correlation exists.
By treating attention as a cognitive faculty, we import human mental ontology into a system that operates purely on relational constraints.
Relational Ontology Footnote
From a relational perspective, attention is not focus, but a pattern of token interactions actualised in context. The model does not “notice” or “care”; it instantiates statistical dependencies that give the appearance of selective prioritization.
Closing Joke (Because Parody)
If LLMs truly had attention like humans, they’d be prone to distractions, checking their social feeds mid-generation, and occasionally daydreaming about quantum physics instead of finishing your sentence.
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