状況に応じた学習クイズ

クイズに答えると、答えと解説が表示されます。

1. インコンテキスト学習中にモデル内部で根本的に何が変化するのでしょうか?
   • モデルの重みはいくつかの勾配ステップによって更新されます
   • 小さなアダプター層がサンプルでトレーニングされます
   • モデルは新しいタスクを永続的に記憶します
   • 重みは何も変わりません。プロンプトのみが出力を制御します
2. 実際の質問が呼び出される前に、いくつかの実際の例が含まれるプロンプトが表示されます。
   • ゼロショットプロンプト
   • 数発のプロンプト
   • 微調整
   • 強化学習
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