函数向量和任务表示测验

当您参加测验时，就会出现答案和解释。

1. What does a function vector represent in the studied language models?
   • A compact activation-space representation associated with a demonstrated task
   • The model’s full weight matrix
   • A single token embedding in every setting
   • The optimizer’s learning rate
2. 函数向量通常是如何构造的？
   • 通过对词汇表中的所有词嵌入求和
   • 通过对示例提示上特定任务的注意力头的输出进行平均
   • 通过在一个示例上微调模型
   • 通过读取最终的softmax概率
3. What did the function-vector paper observe after injecting a vector into a zero-shot prompt?
   • The model always crashed
   • It guaranteed correct answers on any task
   • It could trigger the associated task in the tested settings
   • It permanently changed the model weights
4. 哪种分析技术支持找到带有函数向量的头部？
   • 损失的梯度下降
   • 集束搜索
   • Dropout正则化
   • 因果中介分析
5. What do function-vector experiments provide evidence for?
   • Some models form reusable internal representations associated with tasks
   • All language models only memorize text
   • Every model has one universal task vector
   • Tokenization alone determines every behavior
6. Why are function vectors interesting to steering research?
   • They guarantee safe or correct behavior
   • They provide an activation-level intervention that can influence task behavior in tested settings
   • They eliminate prompts for every application
   • They make all model decisions fully transparent
7. At what part of the transformer did the paper report strong function-vector effects?
   • Only the input embedding table
   • Only the final output logits
   • Middle layers in the tested models
   • 分词器词汇
8. What happened when researchers summed some function vectors?
   • The vectors always canceled to zero
   • The model’s weights were permanently updated
   • Every possible task was composed perfectly
   • Some combinations triggered new complex tasks in the tested experiments