Quiz jàng ci biir muy tekki

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Lan mooy soppeeku ci biir model bi ci diiru jàng ci biir contexte?
   • Diisaayu model bi ñu ngi koy yeesal ci yenn jéego yu am degrade
   • Dañu tàggat benn couche adaptateur bu ndaw ci misaal yi
   • Royuwaay bi dafay fàttaliku liggéey bu bees bi ba fàww
   • Amul lenn luy soppiku ci diisaay yi; prompt bi kese mooy doxal génn gi
2. Ab laajte bu amaale ay misaal yuñ liggéey balaa ñuy woowe laaj bu dëggu bi:
   • Zero-shot
   • Few-shot
   • Akordement
   • Jàng buy dooleel
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