가이드 연계 퀴즈 · 중간 레벨

Grokking 및 지연된 일반화 퀴즈

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관련 가이드 경로그로킹과 지연된 일반화
질문 1 의 8

Which performance pattern characterizes the studied grokking phenomenon?

모든 질문 미리보기8 질문

퀴즈를 풀면 정답과 설명이 나타납니다.

  1. Which performance pattern characterizes the studied grokking phenomenon?
    • Held-out accuracy must improve before any training example is learned
    • Training accuracy can never become high
    • Every model loses all training information at once
    • Held-out generalization improves substantially after the training examples have already been fit
  2. Which tasks did the original grokking paper use as a controlled setting?
    • Only large-scale photorealistic image generation
    • Small algorithmically generated tasks such as modular arithmetic
    • Only real-time online advertising auctions
    • Only unlabelled random filenames
  3. Which regularization intervention was among those examined in the original grokking experiments?
    • Weight decay
    • Mandatory deletion of every activation function
    • A proof that all test labels must be randomized
    • Automatic enlargement of the vocabulary on every step
  4. What computation did the cited modular-addition mechanistic study identify in its trained small transformers?
    • A universal rule for every large deployed model
    • Only a table containing every held-out answer
    • A Fourier-based algorithm using trigonometric relationships
    • No identifiable structured computation
  5. Which phase ordering does Progress Measures for Grokking describe in the studied training dynamics?
    • Cleanup, deletion of all data, then no computation
    • Memorization, circuit formation, then cleanup
    • Perfect held-out performance before any circuit formation
    • A mandatory switch to image-only inputs
  6. 그로킹은 수련자들에게 어떤 실질적인 주의 사항을 제안합니까?
    • 훈련 정확도가 100%에 도달하는 순간 항상 훈련을 중지하십시오.
    • 유효성 검사 정확도는 관련이 없습니다.
    • 초기 검증 정체기가 항상 모델 학습에 실패했음을 의미하는 것은 아닙니다.
    • 정규화는 절대 사용하면 안 됩니다.
  7. For the modular-addition task, what is (6+5) modulo 7?
    • 11
    • 7
    • 1
    • 4
  8. What dataset-size trend did the original grokking paper report in its studied setups?
    • Smaller training datasets required more optimization to reach generalization
    • Smaller datasets always generalized immediately with no optimization
    • Dataset size was never varied
    • Every larger dataset prevented all training accuracy