The attention mechanism enables this iterative processing. For every token, the model generates three vectors: query, key, and value. These structures function as sophisticated key-value stores. When generating new tokens, the model matches current queries against historical keys to determine relevant prior tokens, then weights corresponding values to construct contextual understanding.
早期人工智能追求"智商"，要求正确解答数学问题；当下人工智能追求"情商"，期望在交互中展现人性化特质，乃至特定文化背景下的个性特征。
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