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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1z"/></svg>
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<h1 id="attention-is-all-you-need">Attention Is All You Need<a class="headerlink" href="#attention-is-all-you-need" title="Permanent link">¶</a></h1>
|
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<blockquote>
|
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<p><a href="https://arxiv.org/abs/1706.03762">论文链接</a></p>
|
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</blockquote>
|
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<h2 id="_1">一句话总结<a class="headerlink" href="#_1" title="Permanent link">¶</a></h2>
|
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<p>这篇论文提出了 Transformer，用纯注意力机制替代 RNN/CNN，显著提升了序列建模的并行性与性能。</p>
|
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<h2 id="_2">研究问题<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
|
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<p>传统序列模型（RNN、LSTM）难以并行，而且建模长距离依赖时效率较低。作者希望找到一种更高效的序列到序列建模方式。</p>
|
||
<h2 id="_3">核心方法<a class="headerlink" href="#_3" title="Permanent link">¶</a></h2>
|
||
<p>Transformer 的核心由以下模块组成：</p>
|
||
<ol>
|
||
<li><strong>Multi-Head Self-Attention</strong></li>
|
||
<li><strong>Position-wise Feed-Forward Network</strong></li>
|
||
<li><strong>Residual Connection + LayerNorm</strong></li>
|
||
<li><strong>Positional Encoding</strong></li>
|
||
</ol>
|
||
<p>注意力计算的核心公式：</p>
|
||
<div class="arithmatex">\[
|
||
\mathrm{Attention}(Q, K, V) = \mathrm{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V
|
||
\]</div>
|
||
<h2 id="_4">关键创新点<a class="headerlink" href="#_4" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>用 self-attention 替代循环结构</li>
|
||
<li>多头注意力让模型从不同子空间捕获关系</li>
|
||
<li>编码器/解码器结构具有极强的并行性</li>
|
||
</ul>
|
||
<h2 id="_5">实验结果<a class="headerlink" href="#_5" title="Permanent link">¶</a></h2>
|
||
<p>在机器翻译任务上，Transformer 达到了当时非常强的结果，同时训练速度明显快于循环模型。</p>
|
||
<h2 id="_6">优点<a class="headerlink" href="#_6" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>并行友好</li>
|
||
<li>长程依赖建模更直接</li>
|
||
<li>架构清晰，易扩展</li>
|
||
</ul>
|
||
<h2 id="_7">局限<a class="headerlink" href="#_7" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>位置编码不是天然内生的</li>
|
||
<li>注意力复杂度随序列长度平方增长</li>
|
||
</ul>
|
||
<h2 id="_8">我的理解 / 启发<a class="headerlink" href="#_8" title="Permanent link">¶</a></h2>
|
||
<p>这篇论文最重要的意义不只是“效果更好”，而是把序列建模的主干从“递归”切换成了“基于关系的全局交互”，从而开启了后续大语言模型的主流范式。</p>
|
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|
||
|
||
|
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|
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|
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|
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|
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|
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