Unverified50% confidenceFactExact time
When processing text, the self-attention mechanism simultaneously sees all words in the entire sequence and dynamically calculates the correlation weight between each word and every other word, using parallel computation rather than processing word by word sequentially like early RNNs.
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50%
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Long-term
Relevance
9/18/2026
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Unverified大语言模型通过注意力机制（Attention Mechanism）对上下文窗口内所有token同时计算相关性权重，而非像人类一样逐步阅读和筛选资料78% similarUnverifiedOffline file transcription can use bidirectional attention mechanisms to integrate information from both preceding and following context, and can perform multiple rounds of decoding and reranking to improve accuracy.76% similarUnverifiedPositional Encoding solves the problem of the attention mechanism being inherently orderless and unaware of word sequence by attaching positional information to each word.74% similarUnverifiedGlobal self-attention has a modeling complexity of O(n²), and subsequent research has developed relative positional encoding schemes such as RoPE and ALiBi to enhance long-text processing capability.72% similarUnverifiedThe inference process of large language models is divided into two phases, Prefill and Decode, and the computational complexity of the attention mechanism is O(n²).72% similar
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