In-Context Learning Quiz

Answers and explanations appear as you take the quiz.

1. What fundamentally changes inside the model during in-context learning?
   • The model's weights are updated by a few gradient steps
   • A small adapter layer is trained on the examples
   • The model permanently memorizes the new task
   • Nothing in the weights changes; only the prompt steers the output
2. A prompt that includes several worked examples before the real question is called:
   • Zero-shot prompting
   • Few-shot prompting
   • Fine-tuning
   • Reinforcement learning
3. What does GPT-3's reported improvement in few-shot performance with scale establish?
   • 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. How should induction-head research be related to broad in-context learning?
   • 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. How should an application compare repeated few-shot prompting with fine-tuning?
   • 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. A few-shot ticket prompt demonstrates only refund and billing labels. What do those examples provide for the next ticket?
   • 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