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68 lines
7.1 KiB
JSON
68 lines
7.1 KiB
JSON
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"narration": "前面几页我们把架构和训练细节都讲完了，那效果到底行不行？这一页就是论文交出的成绩单。请大家看屏幕上这张机器翻译结果表，重点看加粗的第三行：Transformer big 在英德任务上拿到28.4 BLEU，比此前最好的集成模型高出约2个点；英法方向达到41.8，同样是新的最优纪录。更值得关注的是最右边训练成本这一列，它只用了2.3乘10的19次方的浮点运算量，连对手的三分之一都不到。也就是说，纯注意力机制不但质量更高，还更省钱。这样的架构在其他任务上还能打吗？下一页接着看。",
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"visual_references": [
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"标题 Machine Translation Results",
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"结果表格中加粗的 Transformer (big) 行",
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"EN-DE 列的 28.4 与 EN-FR 列的 41.8",
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"Training Cost (FLOPs) 列的 2.3×10^19 与上方两行数值对比"
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]
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"content": "你是一位严谨而自然的中文技术讲师，正在讲解论文 Attention Is All You Need。\n这是第 9/12 页。下面给出该页真实 Slidev 源码：\n<slide>\n## Machine Translation Results\n\n| Model | EN-DE BLEU | EN-FR BLEU | Training Cost (FLOPs) |\n|-------|------------|------------|-----------------------|\n| GNMT + RL Ensemble | 26.30 | 41.16 | $1.8 \\cdot 10^{20}$ |\n| ConvS2S Ensemble | 26.36 | 41.29 | $7.7 \\cdot 10^{19}$ |\n| **Transformer (big)** | **28.4** | **41.8** | **$2.3 \\cdot 10^{19}$** |\n\n- Transformer outperforms all previous state-of-the-art models\n- Achieves better results with significantly lower training cost\n- 28.4 BLEU on EN-DE (↑2 BLEU over previous best)\n- 41.8 BLEU on EN-FR (new state-of-the-art)\n</slide>\n请写一段 180–240 个中文字符左右的口语讲解。必须满足：\n1. 用引导性叙事解释“为什么”和“它与前后页的关系”，不能逐条照读幻灯片；\n2. 明确指向当前屏幕上的可见元素（标题、公式、表格、数值或图片）；有图片/表格/公式时必须说出观众该看哪里；\n3. 不虚构源码中没有的数值或结论；开头/结尾与页序匹配；\n4. 只返回 JSON：{\"narration\":\"...\",\"visual_references\":[\"...\"]}。\n"
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"content": "{\"narration\":\"前面几页我们把架构和训练细节都讲完了，那效果到底行不行？这一页就是论文交出的成绩单。请大家看屏幕上这张机器翻译结果表，重点看加粗的第三行：Transformer big 在英德任务上拿到28.4 BLEU，比此前最好的集成模型高出约2个点；英法方向达到41.8，同样是新的最优纪录。更值得关注的是最右边训练成本这一列，它只用了2.3乘10的19次方的浮点运算量，连对手的三分之一都不到。也就是说，纯注意力机制不但质量更高，还更省钱。这样的架构在其他任务上还能打吗？下一页接着看。\",\"visual_references\":[\"标题 Machine Translation Results\",\"结果表格中加粗的 Transformer (big) 行\",\"EN-DE 列的 28.4 与 EN-FR 列的 41.8\",\"Training Cost (FLOPs) 列的 2.3×10^19 与上方两行数值对比\"]}",
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"reasoning_content": "The task: write a Chinese oral narration for slide 9/12 of Attention Is All You Need, about Machine Translation Results. Need 180-240 Chinese characters, guiding narrative explaining \"why\" and relation to previous/next pages, pointing to visible elements (table, numbers), no fabrication, JSON only output.\n\nSlide 9 is about translation results — previous pages presumably covered architecture/training details, so this page shows the payoff. Point to the table: EN-DE BLEU 28.4 vs 26.30/26.36, EN-FR 41.8 vs 41.16/41.29, training cost FLOPs 2.3e19 vs 1.8e20/7.7e19. Emphasize better quality at lower cost.\n\nWrite ~200 characters. Include visual_references list: \"Machine Translation Results 标题\", \"BLEU 对比表格\", \"28.4 / 41.8 加粗数值\", \"Training Cost (FLOPs) 列 2.3·10^19\".\n\nCount characters carefully. Let me draft:\n\n\"讲了这么多结构设计，效果到底怎么样？现在看屏幕这张翻译结果表格。这是第9页，也是整篇论文最有说服力的证据。请大家看表格里加粗的那一行：Transformer big 在英德翻译上拿到28.4 BLEU，比此前最好的集成模型高出约2个点；英法方向达到41.8，同样是新纪录。更关键的是最右边一列——训练成本，它只用了2.3×10^19 次浮点运算，不到对手的三分之一。也就是说，纯注意力架构不仅质量更高，还更便宜。那它在其他任务上表现如何？下一页继续看。\"\n\nCount: roughly let me estimate. That's around 200+ characters. Let me count more precisely.\n\n\"讲了这么多结构设计，效果到底怎么样？\" = 19\n\"现在看屏幕这张翻译结果表格。\" = 14\n\"这是第9页，也是整篇论文最有说服力的证据。\" = 21\n\"请大家看表格里加粗的那一行：\" = 14\n\"Transformer big 在英德翻译上拿到28.4 BLEU，比此前最好的集成模型高出约2个点；\" — count Chinese chars plus some Latin. The requirement says 180–240 个中文字符 — likely total characters. Let me just aim ~210 total.\n\n\"英法方向达到41.8，同样是新纪录。\" = ~17\n\"更关键的是最右边一列——训练成本，它只用了2.3×10的19次方次浮点运算，不到对手的三分之一。\" \n\"也就是说，纯注意力架构不仅质量更高，还更便宜。\"\n\"那它在其他任务上表现如何？下一页继续看。\"\n\nTotal likely ~215. Good.\n\nActually the slide says \"↑2 BLEU over previous best\" so \"高出约2个点\" is fine. Cost comparison: 2.3e19 vs 7.7e19 (ConvS2S) — that's less than a third; vs 1.8e20 GNMT it's about 1/8. Saying \"不到对手的三分之一\" is safe for both.\n\nJSON output only."
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