[{"data":1,"prerenderedAt":6394},["ShallowReactive",2],{"blog-posts":3},[4,56,362,396,1482,1783,1834,1898,2255,2461,2499,3009,3059,3147,3281],{"_path":5,"_dir":6,"_draft":7,"_partial":7,"_locale":8,"title":9,"description":8,"date":10,"categories":11,"tags":12,"draft":7,"body":15,"_type":50,"_id":51,"_source":52,"_file":53,"_stem":54,"_extension":55},"\u002Fposts\u002Frotational-positional-encoding","posts",false,"","RoPE","2026-06-17 18:31:40","殆罔集",[13,14,9],"LLMs","Transformer",{"type":16,"children":17,"toc":46},"root",[18,26,31,36,41],{"type":19,"tag":20,"props":21,"children":22},"element","p",{},[23],{"type":24,"value":25},"text","Attn 本身不表示位置，不包含时序信息。在进入 Attn Block 之前，需要将文本的位置信息编码进 Embedding vector 里。",{"type":19,"tag":20,"props":27,"children":28},{},[29],{"type":24,"value":30},"早期如 BERT 使用的是绝对位置编码，用一个Embedding去表示位置，无法外推，并且还需要额外存储位置信息。相对位置编码，用两个token之间的相对距离去编码解决了外推的问题，但实现很麻烦。",{"type":19,"tag":20,"props":32,"children":33},{},[34],{"type":24,"value":35},"我们知道 Attn 本身就是抽象出 Query、Key、Value，计算QK相关性找V这个过程，这里用了矩阵乘法。对于矩阵我们有很多trick。同时旋转 Q、K 两个矩阵，他们矩阵点乘的结果就能实现相对位置编码，而旋转过程中频率的衰减自然展现出了绝对位置的信息。",{"type":19,"tag":20,"props":37,"children":38},{},[39],{"type":24,"value":40},"RoPE 利用了矩阵的数学特性，漂亮地做好了位置编码这件事。只需要做旋转就可以。空间复杂度O(1)，算法复杂度都降了很多。把大的QK矩阵分块改成一对一对的到二维空间做旋转，这些维度对从高到低不同频率衰减包含在其中。高频负责近距离，中频平衡精度和范围，低频确定远距离位置。外推能力强，没有引入额外的复杂度，非常美丽。",{"type":19,"tag":20,"props":42,"children":43},{},[44],{"type":24,"value":45},"如果位置信息能从off到其他模块，RoPE这块还能减负。比如使用Linear Attn，如Gated DeltaNet，自带很强的位置理解。Attn就可以把长程的位置理解off掉，用Partial RoPE保留高频部分，只理解近邻的位置信息即可。",{"title":8,"searchDepth":47,"depth":48,"links":49},2,3,[],"markdown","content:posts:rotational-positional-encoding.md","content","posts\u002Frotational-positional-encoding.md","posts\u002Frotational-positional-encoding","md",{"_path":57,"_dir":6,"_draft":7,"_partial":7,"_locale":8,"title":58,"description":8,"date":59,"categories":11,"tags":60,"draft":7,"body":61,"_type":50,"_id":359,"_source":52,"_file":360,"_stem":361,"_extension":55},"\u002Fposts\u002Fintroduction-to-large-language-model","大语言模型入门基础","2026-06-16 22:36:11",[14,13],{"type":16,"children":62,"toc":352},[63,68,75,80,85,90,95,100,106,111,116,121,126,131,196,201,206,314,320,325,330,336,341,346],{"type":19,"tag":20,"props":64,"children":65},{},[66],{"type":24,"value":67},"本文介绍大语言模型(LLM)的基础内容。",{"type":19,"tag":69,"props":70,"children":72},"h2",{"id":71},"insight",[73],{"type":24,"value":74},"Insight",{"type":19,"tag":20,"props":76,"children":77},{},[78],{"type":24,"value":79},"对于推理时的 LLMs 来说，它只做一件事：预测下一个 token。这听起来是一件非常简单的一件事。为什么 LLMs 却在文本模态上表现的如此全能呢？",{"type":19,"tag":20,"props":81,"children":82},{},[83],{"type":24,"value":84},"力大砖飞？道法自然？或许都对。LLMs 在海量数据上做了 pretrain。截止到2026年4月份，国内开源的几家模型公司在预训练阶段训练的token数量大约都在30T-35TB之间。模型每个参数预训练消耗的token都远大于20了。并且我相信这个数据使用量会在 DeepSeek V4 分享他们新的稀疏方法后，继续增长。另外，qwen 小模型的实践告诉了我们：预训练时，随着数据量的增多，即使是小模型，其能力也在稳步提升。",{"type":19,"tag":69,"props":86,"children":88},{"id":87},"数据飞轮",[89],{"type":24,"value":87},{"type":19,"tag":20,"props":91,"children":92},{},[93],{"type":24,"value":94},"在模型的长上下文突破(RoPE、YaRN), 基座模型 Agentic 能力提升， LLM Infra(vLLM、SGLang etc.) Day0级跟进支持等等突破之后，一批全新的、过去没有的长上下文 Agent 任务的数据在飞速产生。这个时候谁拥有更多数据（并且能聪明地运用），那么他们下一代的模型就会更强，这是个正反馈的过程，也被称为数据飞轮。",{"type":19,"tag":20,"props":96,"children":97},{},[98],{"type":24,"value":99},"数据飞轮正在拉大不同模型厂商之间的差距。",{"type":19,"tag":69,"props":101,"children":103},{"id":102},"看到新的-token-产生的时候发生了什么",[104],{"type":24,"value":105},"看到新的 Token 产生的时候，发生了什么？",{"type":19,"tag":20,"props":107,"children":108},{},[109],{"type":24,"value":110},"使用 LLM 时，输入的是人类的文本，输出的是人类的文本，这个过程中模型到底做了什么呢？",{"type":19,"tag":20,"props":112,"children":113},{},[114],{"type":24,"value":115},"在看到文本的前一步，token是经过采样后得到的next token ID，根据ID一做 detokenizer 就得到看见的文本了。",{"type":19,"tag":20,"props":117,"children":118},{},[119],{"type":24,"value":120},"得到 token id 的前一步，是经过 Softmax 计算出的一个概率矩阵，根据不同的解码策略（贪婪解码：取概率最大的； 投机解码：需要 MTP 模块； 或者按概率采样等等），选择next token id。再上一步就是 logits。我们平常说的 Temperature（对 logits 矩阵做除法）、Top-K（只在概率最大的K个Token中选）、Top-P（只在累积概率P以内的Token中选）都是在这步做的。",{"type":19,"tag":20,"props":122,"children":123},{},[124],{"type":24,"value":125},"logits 输出前是 LM Head（Linear 层），它将 hidden Linear 的特征数扩大到词表的大小。在此之前是文本经过 Embedding 向量化和 PE（Positional Encode）后进入若干个 Transformer Block 后又过了一遍 RMSNorm。",{"type":19,"tag":20,"props":127,"children":128},{},[129],{"type":24,"value":130},"Transformer Block很简单。就是 Attn 机制加上前馈网络（FFN）罢了。",{"type":19,"tag":132,"props":133,"children":137},"pre",{"className":134,"code":135,"language":136,"meta":8,"style":8},"language-python shiki shiki-themes github-light github-dark","# Input: x\n# Transformer Block\ndef forward(self, x):\n    x = x + Attn(RMSNorm(x))  # 简单的残差\n    x = x + FFN(RMSNorm(x))\n    return x\n","python",[138],{"type":19,"tag":139,"props":140,"children":141},"code",{"__ignoreMap":8},[142,153,161,169,178,187],{"type":19,"tag":143,"props":144,"children":147},"span",{"class":145,"line":146},"line",1,[148],{"type":19,"tag":143,"props":149,"children":150},{},[151],{"type":24,"value":152},"# Input: x\n",{"type":19,"tag":143,"props":154,"children":155},{"class":145,"line":47},[156],{"type":19,"tag":143,"props":157,"children":158},{},[159],{"type":24,"value":160},"# Transformer Block\n",{"type":19,"tag":143,"props":162,"children":163},{"class":145,"line":48},[164],{"type":19,"tag":143,"props":165,"children":166},{},[167],{"type":24,"value":168},"def forward(self, x):\n",{"type":19,"tag":143,"props":170,"children":172},{"class":145,"line":171},4,[173],{"type":19,"tag":143,"props":174,"children":175},{},[176],{"type":24,"value":177},"    x = x + Attn(RMSNorm(x))  # 简单的残差\n",{"type":19,"tag":143,"props":179,"children":181},{"class":145,"line":180},5,[182],{"type":19,"tag":143,"props":183,"children":184},{},[185],{"type":24,"value":186},"    x = x + FFN(RMSNorm(x))\n",{"type":19,"tag":143,"props":188,"children":190},{"class":145,"line":189},6,[191],{"type":19,"tag":143,"props":192,"children":193},{},[194],{"type":24,"value":195},"    return x\n",{"type":19,"tag":20,"props":197,"children":198},{},[199],{"type":24,"value":200},"Attn 机制的话，现在有DSA、MLA等等。不过未来的趋势还是掌管稀疏的神——DeepSeek的新Attention的时代，这成本降的太夸张了。",{"type":19,"tag":20,"props":202,"children":203},{},[204],{"type":24,"value":205},"干了什么？混合注意力（CSA：压缩稀疏注意力， HCA：高度压缩注意力）。不仅仅是上一代注意力的稀疏性，还直接对 Attn 进行压缩。表明了一个潜在的观点，对于长上下文（百万级），即使利用其稀疏性能够将 Attn 的算法复杂度降到 O(L)级。但是面对长上下文存储的 KV Cache还是无能为力。V4 直接对 Attn 进行压缩，减少 KV Cache数量。（Tips: KV Cache可以近似看成 Context 的物理实体）",{"type":19,"tag":132,"props":207,"children":209},{"className":134,"code":208,"language":136,"meta":8,"style":8},"def forward(self, x):\n    # 计算多头注意力 -> xq, xk, xv\n    xq, xk = RMSNorm(xq), RMSNorm(xk) # qk-norm\n    \n    xq, xk = apply_RoPE(xq, xk, cos, sin) # 加入相对位置编码\n    #... 存 KV Cache。  \n    # KV Cache 一般存显存。但是现在有存在SSD上的，比如DeepSeek，使得缓存命中成本惊人的低，是未来降低推理成本的一个方向\n\n    output = F.scaled_dot_product_attention(xq, xk, xk, is_casual=True) # FlashAttention\n    output = output.transpose(1, 2).reshape(bsz, seq_lean, -1)\n\n    return output, past_kv\n",[210],{"type":19,"tag":139,"props":211,"children":212},{"__ignoreMap":8},[213,220,228,236,244,252,260,269,279,288,297,305],{"type":19,"tag":143,"props":214,"children":215},{"class":145,"line":146},[216],{"type":19,"tag":143,"props":217,"children":218},{},[219],{"type":24,"value":168},{"type":19,"tag":143,"props":221,"children":222},{"class":145,"line":47},[223],{"type":19,"tag":143,"props":224,"children":225},{},[226],{"type":24,"value":227},"    # 计算多头注意力 -> xq, xk, xv\n",{"type":19,"tag":143,"props":229,"children":230},{"class":145,"line":48},[231],{"type":19,"tag":143,"props":232,"children":233},{},[234],{"type":24,"value":235},"    xq, xk = RMSNorm(xq), RMSNorm(xk) # qk-norm\n",{"type":19,"tag":143,"props":237,"children":238},{"class":145,"line":171},[239],{"type":19,"tag":143,"props":240,"children":241},{},[242],{"type":24,"value":243},"    \n",{"type":19,"tag":143,"props":245,"children":246},{"class":145,"line":180},[247],{"type":19,"tag":143,"props":248,"children":249},{},[250],{"type":24,"value":251},"    xq, xk = apply_RoPE(xq, xk, cos, sin) # 加入相对位置编码\n",{"type":19,"tag":143,"props":253,"children":254},{"class":145,"line":189},[255],{"type":19,"tag":143,"props":256,"children":257},{},[258],{"type":24,"value":259},"    #... 存 KV Cache。  \n",{"type":19,"tag":143,"props":261,"children":263},{"class":145,"line":262},7,[264],{"type":19,"tag":143,"props":265,"children":266},{},[267],{"type":24,"value":268},"    # KV Cache 一般存显存。但是现在有存在SSD上的，比如DeepSeek，使得缓存命中成本惊人的低，是未来降低推理成本的一个方向\n",{"type":19,"tag":143,"props":270,"children":272},{"class":145,"line":271},8,[273],{"type":19,"tag":143,"props":274,"children":276},{"emptyLinePlaceholder":275},true,[277],{"type":24,"value":278},"\n",{"type":19,"tag":143,"props":280,"children":282},{"class":145,"line":281},9,[283],{"type":19,"tag":143,"props":284,"children":285},{},[286],{"type":24,"value":287},"    output = F.scaled_dot_product_attention(xq, xk, xk, is_casual=True) # FlashAttention\n",{"type":19,"tag":143,"props":289,"children":291},{"class":145,"line":290},10,[292],{"type":19,"tag":143,"props":293,"children":294},{},[295],{"type":24,"value":296},"    output = output.transpose(1, 2).reshape(bsz, seq_lean, -1)\n",{"type":19,"tag":143,"props":298,"children":300},{"class":145,"line":299},11,[301],{"type":19,"tag":143,"props":302,"children":303},{"emptyLinePlaceholder":275},[304],{"type":24,"value":278},{"type":19,"tag":143,"props":306,"children":308},{"class":145,"line":307},12,[309],{"type":19,"tag":143,"props":310,"children":311},{},[312],{"type":24,"value":313},"    return output, past_kv\n",{"type":19,"tag":69,"props":315,"children":317},{"id":316},"optimizer",[318],{"type":24,"value":319},"Optimizer",{"type":19,"tag":20,"props":321,"children":322},{},[323],{"type":24,"value":324},"除了 Embeding 模型和 LM Head Linear层以及其余Linear层、Bias用 AdamW 优化器，其余矩阵用 Muon(dim>=2)。Muon 能够保证矩阵正交性，可以加速收敛，Loss下降更稳定。但是 Muon 较新，一些后端实现的性能不好（比如 PyTorch 2.12 的 mps 后端， 替换 AdamW 之后训练速度变慢了）。",{"type":19,"tag":20,"props":326,"children":327},{},[328],{"type":24,"value":329},"不过使用 Muon 是趋势，相比 AdamW 能够减少一半optim的参数使用，训练也更稳定。",{"type":19,"tag":69,"props":331,"children":333},{"id":332},"mtp-多-token-预测",[334],{"type":24,"value":335},"MTP 多 Token 预测",{"type":19,"tag":20,"props":337,"children":338},{},[339],{"type":24,"value":340},"一次推理输出多个 Token。",{"type":19,"tag":20,"props":342,"children":343},{},[344],{"type":24,"value":345},"如何实现？加上一个 MTP 模块（可以当只有一块注意力块的LLM）：输入是模型预测出的t+1，经过类似的步骤但是Transformer Block只有一块（减少计算量以及显存开销），得到t+2。嵌套调用后可以一次推理得到多个token。和主模型的 logits 一对比后，结果一样的就保留，不一样的就重新预测。因为序列相近，语义大概率类似，因此成功率很高，一般可以实现2-3倍的加速比。",{"type":19,"tag":347,"props":348,"children":349},"style",{},[350],{"type":24,"value":351},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: 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var(--shiki-dark-text-decoration);}",{"title":8,"searchDepth":47,"depth":48,"links":353},[354,355,356,357,358],{"id":71,"depth":47,"text":74},{"id":87,"depth":47,"text":87},{"id":102,"depth":47,"text":105},{"id":316,"depth":47,"text":319},{"id":332,"depth":47,"text":335},"content:posts:introduction-to-large-language-model.md","posts\u002Fintroduction-to-large-language-model.md","posts\u002Fintroduction-to-large-language-model",{"_path":363,"_dir":6,"_draft":7,"_partial":7,"_locale":8,"title":364,"description":8,"date":365,"categories":366,"tags":367,"draft":7,"body":369,"_type":50,"_id":393,"_source":52,"_file":394,"_stem":395,"_extension":55},"\u002Fposts\u002Fthought-about-coding-agent","关于 Coding Agent 使用的遐思","2026-06-03 17:54:20","色伏集",[368],"Agent",{"type":16,"children":370,"toc":391},[371,376,381,386],{"type":19,"tag":20,"props":372,"children":373},{},[374],{"type":24,"value":375},"10年代以后，GitHub激起来的开源浪潮改变了软件工业；全球的 contributor 通过 GitHub 贡献代码，为开源社区做出贡献。\n这公开的代码库，自然成为 AI 训练的素材。另外 DeepWiki 之类通过 AI 解释代码，帮助开发者快速理解代码。\n人们使用 AI 编写文档、注释、行内补全，能力越发强大，DeepWiki 这样直接分析整个代码仓库给出文档的软件大大降低了开发者\n了解某一特性实现的成本。",{"type":19,"tag":20,"props":377,"children":378},{},[379],{"type":24,"value":380},"如果想复刻某一特性，完全可以在 DeepWiki 上询问相关实现细节，减少了开发者理解代码的难度，同时大大减短了开发者理解的时间。",{"type":19,"tag":20,"props":382,"children":383},{},[384],{"type":24,"value":385},"在 Research 领域，alphaxiv.com 等产品也对传统的检索、查阅文献的流程进行了挑战。对于这样生产力密集的事情，AI 能够极大提升人的效率。",{"type":19,"tag":20,"props":387,"children":388},{},[389],{"type":24,"value":390},"但是我一直在日常生活中思考，AI能帮我做到些什么吗？我追问过自己很多遍，最终答案是日常生活用 AI 赋能的 RoI 对一个普通人来说并不高，并不是特别值得做的事情（从能够做成的\u002F需要花费的成本来说）。",{"title":8,"searchDepth":47,"depth":48,"links":392},[],"content:posts:thought-about-coding-agent.md","posts\u002Fthought-about-coding-agent.md","posts\u002Fthought-about-coding-agent",{"_path":397,"_dir":6,"_draft":7,"_partial":7,"_locale":8,"title":398,"description":399,"date":400,"categories":11,"tags":401,"body":403,"_type":50,"_id":1479,"_source":52,"_file":1480,"_stem":1481,"_extension":55},"\u002Fposts\u002Fabout-ssh-usage","关于SSH的使用——客户端篇","介绍SSH客户端的用法。","2025-06-17 02:44:57",[402],"工具",{"type":16,"children":404,"toc":1466},[405,410,424,429,443,604,609,677,723,744,850,856,861,868,873,878,916,929,934,939,944,977,982,987,992,1001,1006,1011,1016,1121,1126,1163,1258,1271,1284,1289,1318,1355,1360,1366,1371,1376,1381,1435,1440,1451,1462],{"type":19,"tag":20,"props":406,"children":407},{},[408],{"type":24,"value":409},"本文正在编写中...",{"type":19,"tag":20,"props":411,"children":412},{},[413,415,422],{"type":24,"value":414},"参见 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all",{"type":19,"tag":20,"props":2697,"children":2698},{},[2699],{"type":24,"value":2700},"也就是说，没有中间步骤要么注意力强大到能直接解决问题，要么得到错乱的解。而加入中间步骤，解决序列问题中的每一个简单问题，从而可以解决任何能够分解为序列问题的问题。",{"type":19,"tag":2592,"props":2702,"children":2704},{"id":2703},"llms-as-analogical-reasoners",[2705],{"type":24,"value":2706},"LLMs as Analogical Reasoners",{"type":19,"tag":1690,"props":2708,"children":2709},{},[2710],{"type":19,"tag":20,"props":2711,"children":2712},{},[2713],{"type":24,"value":2714},"Do you know a related problem? In fact, when solving a problem, we always profit from previously solved problems, using their result, or their method, or the experience we acquired solving them.",{"type":19,"tag":20,"props":2716,"children":2717},{},[2718,2720,2726],{"type":24,"value":2719},"出于这种思想，可以导出提示词——Recall a related problem, and then solve this one. （回忆一个相关问题，然后解决这个问题）。这种方法又叫 ",{"type":19,"tag":139,"props":2721,"children":2723},{"className":2722},[],[2724],{"type":24,"value":2725},"Self-generated Examples",{"type":24,"value":2727},"。",{"type":19,"tag":20,"props":2729,"children":2730},{},[2731],{"type":24,"value":2732},"不过提示词的角度太过于简陋，从我个人的角度来说，提示词其实很不安全，而且让我很不安。完全把命运交给多出来的词向量真的太怪了。有没有不使用 Prompting 就可以触发 CoT 的方法？",{"type":19,"tag":20,"props":2734,"children":2735},{},[2736],{"type":24,"value":2737},"有的。从 Decode 下手，以往 GPT 输出的时候是选择 Top-k 中概率最高的词输出，也就是简单的贪心选择。如果让其对 Top-k 中的词继续做生成，在根据置信度选择答案，这就是 CoT-decoding 方法。",{"type":19,"tag":20,"props":2739,"children":2740},{},[2741],{"type":19,"tag":996,"props":2742,"children":2745},{"alt":2743,"src":2744},"Illustration of CoT-decoding","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2402.10200v2\u002Fx1.png",[],{"type":19,"tag":20,"props":2747,"children":2748},{},[2749],{"type":24,"value":2750},"Key takeaways",{"type":19,"tag":1382,"props":2752,"children":2753},{},[2754,2759],{"type":19,"tag":448,"props":2755,"children":2756},{},[2757],{"type":24,"value":2758},"Pre-trained LLMs, without further finetuning, has been ready for step-by-step reasoning, but we need a non-greedy decoding strategy to elicit it.",{"type":19,"tag":448,"props":2760,"children":2761},{},[2762],{"type":24,"value":2763},"When a step-by-step reasoning path is present, KKMs have much higher confidence in decoding the final answer than direct-answer decoding.",{"type":19,"tag":2592,"props":2765,"children":2767},{"id":2766},"limits-of-intermediate-steps",[2768],{"type":24,"value":2769},"Limits of Intermediate Steps",{"type":19,"tag":1690,"props":2771,"children":2772},{},[2773],{"type":19,"tag":20,"props":2774,"children":2775},{},[2776,2778],{"type":24,"value":2777},"Always keep in mind that LLMs are probabilistic models of generating next tokens. ",{"type":19,"tag":452,"props":2779,"children":2780},{},[2781],{"type":24,"value":2782},"They are not humans.",{"type":19,"tag":20,"props":2784,"children":2785},{},[2786],{"type":24,"value":2787},"我们希望：给出问题的基础上得到最大可能的最终答案。",{"type":19,"tag":20,"props":2789,"children":2790},{},[2791],{"type":24,"value":2792},"LLM CoT-decoding所做：给出问题的基础上，给出最大可能的 reasoning path 以及最大可能的最终答案。",{"type":19,"tag":2592,"props":2794,"children":2796},{"id":2795},"self-consistency",[2797],{"type":24,"value":2798},"Self-Consistency",{"type":19,"tag":20,"props":2800,"children":2801},{},[2802],{"type":24,"value":2803},"More consistent, more likely to be correct",{"type":19,"tag":20,"props":2805,"children":2806},{},[2807,2809,2817,2819,2824],{"type":24,"value":2808},"人性的一个突出方面是人们的思维方式不同。很自然地假设，在需要深思熟虑的任务中，可能有几种方法可以解决问题。我们建议可以通过从语言模型的解码器中采样来在语言模型中模拟这样的过程。例如，如图 ",{"type":19,"tag":416,"props":2810,"children":2814},{"href":2811,"rel":2812,"title":2813},"https:\u002F\u002Far5iv.labs.arxiv.org\u002Fhtml\u002F2203.11171?_immersive_translate_auto_translate=1#S1.F1",[438],"In 1 Introduction ‣ Self-Consistency Improves Chain of Thought Reasoning in Language Models",[2815],{"type":24,"value":2816},"1 所示",{"type":24,"value":2818}," ，一个模型可以对数学问题生成多个合理的回答，这些回答都得出相同的正确答案（输出 1 和 3）。由于语言模型不是完美的推理器，因此模型也可能产生不正确的推理路径或在其中一个推理步骤中出错（例如，在输出 2 中），但此类解决方案不太可能得出相同的答案。也就是说，我们",{"type":19,"tag":452,"props":2820,"children":2821},{},[2822],{"type":24,"value":2823},"假设正确的推理过程，即使它们是多种多样的，也往往比不正确的过程在最终答案中具有更大的一致性",{"type":24,"value":2727},{"type":19,"tag":20,"props":2826,"children":2827},{},[2828],{"type":19,"tag":996,"props":2829,"children":2832},{"alt":2830,"src":2831},"Self-Consistency method contains three steps","https:\u002F\u002Far5iv.labs.arxiv.org\u002Fhtml\u002F2203.11171\u002Fassets\u002Fx1.png",[],{"type":19,"tag":20,"props":2834,"children":2835},{},[2836],{"type":24,"value":2837},"Self-Consistency 利用了复杂的推理任务通常允许多条推理路径到达正确答案的直觉。一个问题越需要深思熟虑的思考和分析 ，可以得到答案的推理路径就越多样化。",{"type":19,"tag":2592,"props":2839,"children":2841},{"id":2840},"quiz",[2842],{"type":24,"value":2843},"Quiz",{"type":19,"tag":1690,"props":2845,"children":2846},{},[2847],{"type":19,"tag":20,"props":2848,"children":2849},{},[2850],{"type":24,"value":2851},"Q1. 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