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课堂讲述","sha256":"786ce10a6b32a492259a3dccd0a54e61cd41d28a34382fb7b5386c5f03b7ad69","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-reuse-and-targets","title":"训练留下什么，训练答案又从哪里来？"},"page":{"id":"h-i-text-generates-a-target","title":"把同一句话变成两种有答案的题","number":1},"sectionTitle":"把同一句话变成两种有答案的题","currentQuestion":"训练留下什么，训练答案又从哪里来？","sourceSlides":["l3-r2-p05"],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-783f77115d1c9b1e1021dabc","nextBlock":"b-4c3807733a343e1bde3538cf","prerequisites":[{"id":"b-5335c7d9f7a13263d23af3f5","url":"#b-5335c7d9f7a13263d23af3f5","role":"section-title"},{"id":"b-0538a99e522e2e91fa6baed2","url":"#b-0538a99e522e2e91fa6baed2","role":"problem-context"}],"relatedBlocks":[{"id":"b-783f77115d1c9b1e1021dabc","url":"#b-783f77115d1c9b1e1021dabc","relation":"figure-and-explanation"}]},{"id":"b-4c3807733a343e1bde3538cf","type":"p","scope":"narrative","url":"https://codingai-lec03.pages.dev/course/pages/l3-r2-p05.html#b-4c3807733a343e1bde3538cf","content":"看这句话。把后面的内容暂时挡住，就能用前面的文字预测接下来出现的文字单位；把中间一部分遮掉，又能用其余内容预测缺失部分。训练用的答案就在原文中，不需要标注者为每个位置再写一次。\n两种构造并不相同：预测后续时，只能利用允许看到的前文；预测被遮住的部分时，可以利用另一种可见上下文。这里先不研究内部怎样组织长序列，我们只看输入、目标和反馈从哪里来。\n这对视觉研究的启发是：不额外给类别标签，仍然可以有明确的预测任务和误差。文本本来也是人写出来的，“没有额外逐条标注”不是“没有人的知识”。那么，对图像，我们能构造什么样的预测或对应关系？","sha256":"bb981862846582499bb535e74bac27c9c56430339ea30b71f9ebf093391ac383","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-reuse-and-targets","title":"训练留下什么，训练答案又从哪里来？"},"page":{"id":"h-i-text-generates-a-target","title":"把同一句话变成两种有答案的题","number":1},"sectionTitle":"把同一句话变成两种有答案的题","currentQuestion":"训练留下什么，训练答案又从哪里来？","sourceSlides":["l3-r2-p05"],"knowledgeBlock":null,"formulas":[],"previousBlock":"b-b37b35e08bc8492ed2a1f591","nextBlock":"b-d36fc3c6d3470b27af334de9","prerequisites":[{"id":"b-b37b35e08bc8492ed2a1f591","url":"#b-b37b35e08bc8492ed2a1f591","role":"section-title"},{"id":"b-0538a99e522e2e91fa6baed2","url":"#b-0538a99e522e2e91fa6baed2","role":"problem-context"}],"relatedBlocks":[{"id":"b-783f77115d1c9b1e1021dabc","url":"#b-783f77115d1c9b1e1021dabc","relation":"figure-and-explanation"}]},{"id":"b-d36fc3c6d3470b27af334de9","type":"h3","scope":"text-target-construction","url":"https://codingai-lec03.pages.dev/course/pages/l3-r2-p05.html#b-d36fc3c6d3470b27af334de9","content":"### 同一句原文怎样生成两种预测题","sha256":"90a0a1bf6593ae1fe5ee6450d19ac78a7299dc878fe6cf4ba2b835572a860816","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-reuse-and-targets","title":"训练留下什么，训练答案又从哪里来？"},"page":{"id":"h-i-text-generates-a-target","title":"把同一句话变成两种有答案的题","number":1},"sectionTitle":"同一句原文怎样生成两种预测题","currentQuestion":"训练留下什么，训练答案又从哪里来？","sourceSlides":["l3-r2-p05"],"knowledgeBlock":"text-target-construction","formulas":[],"previousBlock":"b-4c3807733a343e1bde3538cf","nextBlock":"b-a7ce7b4bb115901fe83b0784","prerequisites":[{"id":"b-b37b35e08bc8492ed2a1f591","url":"#b-b37b35e08bc8492ed2a1f591","role":"section-title"},{"id":"b-0538a99e522e2e91fa6baed2","url":"#b-0538a99e522e2e91fa6baed2","role":"problem-context"}],"relatedBlocks":[{"id":"b-783f77115d1c9b1e1021dabc","url":"#b-783f77115d1c9b1e1021dabc","relation":"figure-and-explanation"},{"id":"b-a7ce7b4bb115901fe83b0784","url":"#b-a7ce7b4bb115901fe83b0784","relation":"same-reasoning"},{"id":"b-7715493b85258a881f96a2e7","url":"#b-7715493b85258a881f96a2e7","relation":"same-reasoning"},{"id":"b-5ea880c73c134536de40dce4","url":"#b-5ea880c73c134536de40dce4","relation":"same-reasoning"}]},{"id":"b-a7ce7b4bb115901fe83b0784","type":"p","scope":"text-target-construction","url":"https://codingai-lec03.pages.dev/course/pages/l3-r2-p05.html#b-a7ce7b4bb115901fe83b0784","content":"用教学单位切分 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$-\\ln0.25\\approx1.386$；提高到0.5后是0.693。这个反馈足以更新参数，不需要先把整句话人工标成“描述桌面”。","sha256":"28f958ad9c13ccd870e036c2a721d7b09506be2ad44dbf5d6631055578af6b71","chapter":{"id":"I","title":"学习信号与表示"},"unit":{"id":"i-reuse-and-targets","title":"训练留下什么，训练答案又从哪里来？"},"page":{"id":"h-i-text-generates-a-target","title":"把同一句话变成两种有答案的题","number":1},"sectionTitle":"同一句原文怎样生成两种预测题","currentQuestion":"训练留下什么，训练答案又从哪里来？","sourceSlides":["l3-r2-p05"],"knowledgeBlock":"text-target-construction","formulas":[{"latex":"-\\ln0.25\\approx1.386","definitionContext":"用教学单位切分 `The / red / cup / is / on / the / table / .`。从左到右预测的一题是：输入 `The red cup`，正确目标是 `is`。遮词预测的一题是：输入 `The red [MASK] is on the table .`，目标是被取走的 `cup`。答案都从原文取得，但模型可见条件不同：前者不能偷看未来，后者可以使用遮词两侧。\n训练时原文目标已知。若模型给正确词概率0.25，这一位置的负对数损失是 $-\\ln0.25\\approx1.386$；提高到0.5后是0.693。这个反馈足以更新参数，不需要先把整句话人工标成“描述桌面”。\n本页只借文字说明目标来源。实际 tokenizer 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