情境学习测验

当您参加测验时，就会出现答案和解释。

1. 在情境学习过程中模型内部发生了什么根本变化？
   • 模型的权重通过几个梯度步骤更新
   • 一个小型适配器层在示例上进行训练
   • 模型永久记住新任务
   • 权重没有变化；仅提示控制输出
2. 在真正的问题被调用之前，包含几个工作示例的提示：
   • 零射击提示
   • 少镜头提示
   • 微调
   • 强化学习
3. GPT-3 报告的少样本性能随规模提升这一发现确立了什么？
   • A universal rule that all models below one hundred billion parameters cannot use examples
   • A requirement for internet access during every prompt
   • An observed trend for the evaluated models and tasks
   • A separate emergency training phase
4. 归纳头（induction-head）研究应如何与广泛的上下文学习联系起来？
   • As evidence about a studied mechanism, with limitations on broader conclusions
   • As proof that every task uses only one circuit
   • As a permanent database of every demonstration
   • As a replacement for all task evaluation
5. 应用程序应如何比较重复的少样本提示与微调？
   • Assume fine-tuning is always the least expensive choice
   • Measure task quality and the relevant training, request, and maintenance costs
   • Assume adding unlimited prompt examples has no cost
   • Decide solely by which approach has the longer name
6. 一个少样本工单提示仅展示了退款和账单标签。这些示例为下一个工单提供了什么？
   • An automatic update to the model's stored weights
   • A guarantee that every next ticket fits one of the labels
   • A permanent change to all future conversations
   • An in-prompt task and label pattern that can guide classification
7. Why might the same few-shot classification examples produce different results after reordering?
   • The model can be sensitive to the order of examples in its context
   • Reordering automatically retrains every parameter
   • The label texts must disappear
   • The model's vocabulary changes with each permutation
8. What resource tradeoff arises when more demonstrations are added to an in-context prompt?
   • They use no input tokens because they are examples
   • They make the context limit disappear
   • They consume more supported context and per-request processing
   • They permanently reduce the model's parameter count