引导式测验 · 中等 等级

多标记预测训练测验

利用 DeepSeek-V3 独特的顺序设计、可选推理头和目标偏移，检查多令牌损失和共享主干头。

相关引导路径多令牌预测训练
问题 1 的 8

在引用的Meta研究中，哪种架构在训练位置预测多个未来词？

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  1. 在引用的Meta研究中，哪种架构在训练位置预测多个未来词？
    • A mandatory separate complete language model for every word
    • Independent output heads above a shared transformer trunk
    • One head that can target only the preceding token
    • A larger vocabulary with no additional prediction targets
  2. 哪个模型在预训练中特别使用了多标记预测目标？
    • 原始 Word2Vec
    • 卷积图像分类器
    • n 元语法模型
    • DeepSeek-V3
  3. 多词训练在前缀位置提供了哪些额外监督？
    • Loss terms for multiple future-token targets
    • Only a larger batch size with the same single target
    • A mandatory image-reconstruction target
    • No prediction loss at any position
  4. Meta论文的自我推测推理实验中，辅助预测如何使用？
    • They are accepted unconditionally without verification
    • They automatically retrain the model on every prompt
    • They propose tokens that are then checked by the decoding procedure
    • They encrypt every generated token
  5. 如果不需要加速，训练后辅助头会怎样？
    • 他们必须留下来并将模型尺寸扩大一倍
    • 它们可以被丢弃，因此部署规模不会改变
    • 它们变成了注意力层
    • 他们成为标记器
  6. Which evaluated task family showed notable gains in the Meta multi-token study?
    • Code generation benchmarks
    • A guarantee of perfect code compilation in every language
    • Only alphabetizing raw filenames
    • A required quantum-sensor prediction task
  7. In a four-head offset design predicting from prefix position t, which token is the fourth head’s target?
    • The token at t−4
    • The token at t+1 for every head
    • Only the token at t itself
    • The token at t+4
  8. What distinguishes DeepSeek-V3’s stated MTP architecture from the cited independent parallel-head implementation?
    • It predicts only tokens before the prefix
    • It requires every target to be an image pixel
    • Sequential auxiliary modules that preserve a causal chain across prediction depths
    • It removes the language-model output vocabulary