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エントロピーベースのサンプリングに関するクイズ

ネクストトークンのエントロピーと真実の信頼性を区別し、エントロピーとヴァレントロピーを説明し、適応復号ヒューリスティックの限界を確認します。

関連するガイドパスエントロピーベースのサンプリング
質問 1 の 8

What does next-token entropy measure in an entropy-aware decoder?

すべての質問をプレビューする8 質問

クイズに答えると、答えと解説が表示されます。

  1. What does next-token entropy measure in an entropy-aware decoder?
    • How spread out probability is across the model’s possible next tokens
    • Whether the model’s factual claim is true
    • モデル内のパラメータの数
    • The total number of tokens already generated
  2. A low-entropy next-token distribution most directly indicates that:
    • The predicted answer is guaranteed factual
    • Probability is concentrated on a small set of tokens
    • The model has no uncertainty about the entire conversation
    • Every token in the vocabulary is equally likely
  3. 次のトークン分布のシャノン エントロピーは次のように計算されます。
    • ロジットの合計
    • 最大確率から最小確率を引いたもの
    • -sum p_i log p_i をトークン上で計算する
    • 語彙内のトークンの数
  4. What does varentropy measure in some entropy-aware decoding schemes?
    • The variance of model parameter counts
    • The factual accuracy of the next sentence
    • トレーニング エポックの数
    • Variation in token-level surprisal values under the distribution
  5. When an entropy-aware system sees a high-entropy next-token distribution, what can it reasonably conclude?
    • The distribution is more spread out and may merit a task-specific decoding policy
    • The model is certainly hallucinating
    • The highest-probability token is false
    • The output must be stopped immediately
  6. What is a practical limit of using entropy to guide generation?
    • Entropy cannot be computed from probabilities
    • A chosen entropy policy does not by itself guarantee fewer factual errors
    • Entropy removes the need for model weights
    • Entropy forces all tokens to have equal probability
  7. A low-entropy model answer agrees with a user’s false claim. What does the entropy value establish?
    • That the claim has been independently verified
    • That every other candidate is impossible
    • Only that the model’s next-token distribution is concentrated, not that the claim is true
    • That the answer came from a reliable source
  8. How should a team choose thresholds for an entropy-triggered retrieval policy?
    • Assume one threshold works for all models and contexts
    • Choose the threshold from the model name
    • Treat every high-entropy token as a proven error
    • Evaluate the policy on representative tasks and measure whether retrieval improves outcomes