自洽解码测验

当您参加测验时，就会出现答案和解释。

1. 自一致性解码如何从语言模型中选择最终答案？
   • 对多种推理路径进行采样并对答案进行多数投票
   • 在每一步中始终选择最有可能的标记
   • 根据自己的输出反复训练模型
   • 消除生成中的所有随机性
2. Under what condition can self-consistency improve answer accuracy?
   • Whenever the number of samples increases
   • When correct sampled paths converge and errors are not too strongly correlated
   • When every reasoning chain has identical wording
   • Only when the model weights are retrained
3. 自一致性取代了什么解码方法？
   • 反向传播
   • 代币化
   • 单链贪婪解码
   • 权重量化
4. 每个采样链的哪一部分实际用于最终投票？
   • 完整的推理文本
   • 生成的代币数量
   • 温度设定
   • 只有最终答案
5. What is the main cost of increasing the number of self-consistency samples?
   • More inference compute; accuracy gains depend on the task and can have diminishing returns
   • It retrains the model weights after every response
   • It guarantees that the majority is correct
   • It eliminates the need to sample any reasoning paths
6. 自我一致性是哪个更广泛概念的早期例子？
   • 模型压缩
   • 推理时间缩放（花费额外的计算来更好地推理）
   • 训练期间的数据增强
   • 硬件加速
7. Why may answer normalization be useful before a self-consistency vote?
   • It makes all reasoning paths independent
   • It verifies the answer against external evidence
   • It can group semantically equivalent answers that use different formatting
   • It changes the model’s sampled probabilities
8. What is a key limitation of a majority answer from self-consistency?
   • A majority vote cannot be computed for text
   • Sampling always changes the model weights
   • Every reasoning path is guaranteed independent
   • Correlated reasoning errors can make an incorrect answer win the vote