liqiang
b119135836
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69 lines
6.3 KiB
JSON
69 lines
6.3 KiB
JSON
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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 12/12 页。下面给出该页真实 Slidev 源码：\n<slide>\n## Conclusion and Future Work\n\n### Key Contributions\n- Introduced Transformer architecture based solely on attention\n- Achieved new state-of-the-art results in machine translation\n- Demonstrated improved parallelization and reduced training time\n- Showed generalization to other tasks like constituency parsing\n\n### Future Directions\n- Apply to other modalities (images, audio, video)\n- Investigate local, restricted attention for large inputs\n- Make generation less sequential\n- Explore interpretability of attention mechanisms\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\":\"好，我们来到最后一页。请大家看屏幕上的结论部分：回顾前面十一页，从自注意力公式到编码器解码器结构，再到翻译实验，其实都指向",
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"reasoning_content": "The task: write a narration for page 12/12, the conclusion slide of \"Attention Is All You Need\". 180-240 Chinese characters, guiding narrative, referencing visible elements, no fabricated numbers, matching page order (final page, so should wrap up). Return JSON only.\n\nLet me draft:\n\n\"好，我们来到最后一页。大家看屏幕上的 'Conclusion and Future Work'。回顾前面十一页，从 self-attention 的公式到 Encoder-Decoder 结构，再到翻译任务的实验结果，其实都指向作者在这里总结的一点：完全抛弃循环和卷积、只靠注意力，不但行得通，还能取得当时最好的翻译成绩，而且并行度更高、训练更快。左栏还提到它能泛化到句法分析等其他任务。更重要的是右栏的 Future Directions：作者已经预见到把这套机制推广到图像、音频、视频，用局部注意力处理长输入——后来 Vision Transformer、多模态大模型正是沿着这些方向走出来的。这就是这篇论文改变整个领域的起点。\"\n\nCount characters. Let me count roughly. Need 180-240 Chinese characters. Let me count:\n\n\"好，我们来到最后一页。\" ~ 10\nLet me just estimate. I'll write carefully and count.\n\nDraft:\n\n好(1)，(2)我(3)们(4)来(5)到(6)最(7)后(8)一(9)页(10)。(11)大(12)家(13)看(14)屏(15)幕(16)上(17)的(18)结(19)论(20)部(21)分(22)：(23)回(24)顾(25)前(26)面(27)十(28)一(29)页(30)，(31)从(32)自(33)注(34)意(35)力(36)公(37)式(38)到(39)编(40)码(41)器(42)解(43)码(44)器(45)结(46)构(47)，(48)再(49)到(50)翻(51)译(52)实(53)验(54)，(55)其(56)实(57)都(58)指(59)向(60)作(61)者(62)在(63)这(64)里(65)总(66)结(67)的(68)核(69)心(70)—(71)—(72)完(73)全(74)依(75)靠(76)注(77)意(78)力(79)、(80)抛(81)弃(82)循(83)环(84)与(85)卷(86)积(87)，(88)不(89)仅(90)行(91)得(92)通(93)，(94)还(95)刷(96)新(97)了(98)机(99)器(100)翻(101)译(102)的(103)最(104)好(105)成(106)绩(107)，(108)并(109)行(110)度(111)更(112)高(113)、(114)训(115)练(116)更(117)快(118)。(119)左(120)栏(121)还(122)提(123)到(124)它(125)能(126)泛(127)化(128)到(129)成(130)分(131)句(132)法(133)分(134)析(135)等(136)任(137)务(138)。(139)更(140)有(141)意(142)思(143)的(144)是(145)右(146)栏(147)的(148)未(149)来(150)方(151)向(152)：(153)推(154)广(155)到(156)图(157)像(158)、(159)音(160)频(161)、(162)视(163)频(164)，(165)用(166)局(167)部(168)受(169)限(170)注(171)意(172)力(173)应(174)对(175)超(176)长(177)输(178)入(179)，(180)甚(181)至(182)减(183)少(184)生(185)成(186)的(187)逐(188)词(189)顺(190)序(191)性(192)。(193)后(194)来(195)的(196)视(197)觉(198)和(199)多(200)模(201)态(202)模(203)型(204)，(205)正(206)是(207)沿(208)着(209)这(210)些(211)思(212)路(213)走(214)出(215)来(216)的(217)。(218)\n\nAbout 218 characters including punctuation. Good, within 180-240.\n\nVisual references: [\"标题 'Conclusion and Future Work'\", \"左栏 Key Contributions 列表\", \"右栏 Future Directions 列表\"]\n\nReturn JSON only."
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}
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}
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],
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