feat: deepen attention and transformer chapter

src/data/papers.ts

@@ -273,6 +273,30 @@ export const papers: Paper[] = [
     contribution: "以门控网络让多个局部专家竞争分工，是现代 MoE 的概念起点。",
     verified: true,
   },
+  {
+    year: 2015,
+    title: "Pointer Networks",
+    url: "https://arxiv.org/abs/1506.03134",
+    topics: ["Transformer"],
+    contribution: "让 Attention 分布直接指向输入位置，展示软路由不仅能混合表示，也能定义可变输出字典。",
+    verified: true,
+  },
+  {
+    year: 2016,
+    title: "Deep Residual Learning for Image Recognition",
+    url: "https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html",
+    topics: ["Transformer"],
+    contribution: "以加法捷径学习残差映射，是 Transformer 深度信息高速公路的视觉前史。",
+    verified: true,
+  },
+  {
+    year: 2016,
+    title: "Layer Normalization",
+    url: "https://arxiv.org/abs/1607.06450",
+    topics: ["Transformer"],
+    contribution: "在单个样本内按层输入统计量归一化，为序列模型提供训练/推理一致的尺度控制。",
+    verified: true,
+  },
   {
     year: 2017,
     title: "Attention Is All You Need",
@@ -281,6 +305,14 @@ export const papers: Paper[] = [
     contribution: "用多头自注意力与 FFN 取代循环，开启高度并行预训练。",
     verified: true,
   },
+  {
+    year: 2018,
+    title: "Self-Attention with Relative Position Representations",
+    url: "https://aclanthology.org/N18-2074/",
+    topics: ["Transformer", "长上下文"],
+    contribution: "把相对距离表示引入 Self-Attention，并把方法扩展为 relation-aware attention。",
+    verified: true,
+  },
   {
     year: 2018,
     title: "SentencePiece: A simple and language independent subword tokenizer",
@@ -321,6 +353,14 @@ export const papers: Paper[] = [
     contribution: "T5 统一 text-to-text 接口，并以 C4 建立现代 Common Crawl 清洗基线。",
     verified: true,
   },
+  {
+    year: 2019,
+    title: "Unified Language Model Pre-training for Natural Language Understanding and Generation",
+    url: "https://arxiv.org/abs/1905.03197",
+    topics: ["Transformer"],
+    contribution: "用 Attention mask 在同一 Transformer 中切换单向、双向与序列到序列预训练。",
+    verified: true,
+  },
   {
     year: 2019,
     title: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context",
@@ -337,6 +377,38 @@ export const papers: Paper[] = [
     contribution: "不做均值中心化的轻量归一化，成为现代 LLM 常用组件。",
     verified: true,
   },
+  {
+    year: 2019,
+    title: "Are Sixteen Heads Really Better than One?",
+    url: "https://papers.neurips.cc/paper_files/paper/2019/hash/2c601ad9d2ff9bc8b282670cdd54f69f-Abstract.html",
+    topics: ["Transformer", "评测"],
+    contribution: "测试时剪枝显示大量 Attention heads 存在冗余，但不证明这些 head 在训练阶段无用。",
+    verified: true,
+  },
+  {
+    year: 2019,
+    title: "What Does BERT Look at? An Analysis of BERT’s Attention",
+    url: "https://aclanthology.org/W19-4828/",
+    topics: ["Transformer", "评测"],
+    contribution: "在 BERT heads 中观察位置、分隔符、句法和共指模式，为后验分析提供系统案例。",
+    verified: true,
+  },
+  {
+    year: 2019,
+    title: "Attention is not Explanation",
+    url: "https://aclanthology.org/N19-1357/",
+    topics: ["Transformer", "评测"],
+    contribution: "展示 Attention 权重与特征重要性可能不相关，且不同权重可产生近似预测，划定解释边界。",
+    verified: true,
+  },
+  {
+    year: 2019,
+    title: "Attention is not not Explanation",
+    url: "https://aclanthology.org/D19-1002/",
+    topics: ["Transformer", "评测"],
+    contribution: "指出解释结论依赖定义、模型整体、基线与测试协议，为 Attention 解释争论补充对照。",
+    verified: true,
+  },
   {
     year: 2020,
     title: "Language Models are Few-Shot Learners",
@@ -353,6 +425,22 @@ export const papers: Paper[] = [
     contribution: "系统比较 GLU 变体，SwiGLU 进入现代 FFN 配方。",
     verified: true,
   },
+  {
+    year: 2020,
+    title: "On Layer Normalization in the Transformer Architecture",
+    url: "https://proceedings.mlr.press/v119/xiong20b.html",
+    topics: ["Transformer", "训练系统"],
+    contribution: "从初始化梯度分析 Post-LN 的 warm-up 需求与 Pre-LN 的优化差异。",
+    verified: true,
+  },
+  {
+    year: 2021,
+    title: "NormFormer: Improved Transformer Pretraining with Extra Normalization",
+    url: "https://arxiv.org/abs/2110.09456",
+    topics: ["Transformer", "训练系统"],
+    contribution: "在 Pre-LN block 内增加归一化以改善层间梯度尺度与预训练稳定性。",
+    verified: true,
+  },
   {
     year: 2020,
     title: "Longformer: The Long-Document Transformer",