Unverified50% confidenceFactExact time
The self-attention mechanism transforms each character vector into Query, Key, and Value respectively through three learnable weight matrices, transforming N × dm into N × dk.
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9/15/2026
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UnverifiedAttention 采用 QKV 框架，每个 Token 的 Embedding 经三个线性变换矩阵生成 Query、Key、Value 向量76% similarUnverified自注意力机制通过Query-Key-Value矩阵运算决定序列中每个token应关注哪些其他token，注意力权重是总和为1的概率分布70% similarUnverifiedThe attention computation flow is: the Q vector performs dot products with the K vectors of all preceding tokens to obtain relevance scores, which are normalized via Softmax and then used to compute a weighted sum of V vectors, followed by a linear transformation for output.68% similarUnverifiedEach token is converted into a d_model-dimensional vector by looking up the Embedding Table, and N tokens form an N × d_model input matrix.66% similarUnverifiedThe computation flow of a single Decoder layer is: the input vector passes through Self-Attention and Add & Norm, then through FFN and Add & Norm, producing the output vector66% similar
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