People choose the more helpful and safe answer from two responses.
Move model behavior closer to the desired preference.
Choose two correct statements.
Human rankings can guide adaptation, but evaluator criteria, bias, consent, confidentiality, and post-update safety require governance and reevaluation.
Detailed explanation
The feedback can inform adaptation.
The feedback can inform adaptation.
Feedback data is also a quality and governance concern.
Feedback data is also a quality and governance concern.
Evaluator perspective and data bias can be inherited.
Evaluator perspective and data bias can be inherited.
New output quality and safety must be tested.
New output quality and safety must be tested.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 2.1の基盤モデル適応、人のフィードバック、責任あるAIを確認する。Expected result
人の評価データの利点と、評価者・データ品質の限界を説明できる。Key points
- Preference
- Evaluator
- Reevaluation
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.