Prueba de muestreo basada en entropía

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

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
   • El número de parámetros en el modelo.
   • 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. La entropía de Shannon de la distribución del siguiente token se calcula como:
   • La suma de los logits.
   • La probabilidad máxima menos la mínima.
   • -sum p_i log p_i sobre tokens
   • El número de fichas en el vocabulario.
4. What does varentropy measure in some entropy-aware decoding schemes?
   • The variance of model parameter counts
   • The factual accuracy of the next sentence
   • El número de épocas de entrenamiento.
   • 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