前瞻解码测验

当您参加测验时，就会出现答案和解释。

1. 前瞻解码与标准推测解码有何区别？
   • 它需要两个单独训练的模型
   • 它不使用单独的草稿模型 - 仅使用目标模型本身
   • 它仅适用于翻译任务
   • 它永久量化模型
2. 哪种数值方法支持前瞻解码？
   • 梯度下降
   • 傅立叶变换
   • K-均值聚类
   • 雅可比（定点）迭代
3. 每一步运行的两个并行分支是什么？
   • 训练分支和测试分支
   • 编码器和解码器
   • 前瞻分支和验证分支
   • 草稿模型和目标模型
4. “n-gram 池”中存储了什么？
   • 在并行迭代过程中看到的合理的多标记序列
   • 用户的聊天记录
   • 模型重量
   • 注意口罩
5. 当先行解码提交猜测的 n 元语法前缀时，什么支持正确性？
   • Using the same model makes every unverified guess correct
   • Every candidate in the n-gram pool must be committed
   • Verification against the target's greedy predictions or an appropriate distribution-preserving sampling procedure
   • A larger window proves the generated statements are true
6. What resource tradeoff is important when deploying lookahead decoding on already busy hardware?
   • Candidate generation uses no compute or memory
   • Additional parallel work may offset the benefit of fewer sequential passes
   • Every larger window must lower wall-clock latency
   • The target weights are automatically compressed to zero size
7. Why should a lookahead candidate remain provisional until target verification?
   • Parallel iteration guesses can differ from the target's valid continuation
   • Every cached n-gram has already been proven correct by storage alone
   • The lookahead branch permanently changes the target weights
   • Verification is needed only to determine the output filename
8. Does a standard training-free lookahead iteration update the target model's learned weights?
   • Yes; each candidate applies a gradient update to every parameter
   • Yes; verified n-grams become a new training dataset immediately
   • Only the tokenizer's vocabulary is retrained at every step
   • No; it changes candidate token states and verifies outputs while keeping the model weights fixed