एन्ट्रॉपी-आधारित नमूनाकरण प्रश्नोत्तरी

जैसे ही आप प्रश्नोत्तरी लेते हैं उत्तर और स्पष्टीकरण दिखाई देते हैं।

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
   • मॉडल में पैरामीटरों की संख्या
   • 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. अगले-टोकन वितरण की शैनन एन्ट्रापी की गणना इस प्रकार की जाती है:
   • लॉग का योग
   • अधिकतम संभाव्यता शून्य से न्यूनतम
   • -टोकन पर p_i लॉग p_i जोड़ें
   • शब्दावली में टोकन की संख्या
4. What does varentropy measure in some entropy-aware decoding schemes?
   • The variance of model parameter counts
   • The factual accuracy of the next sentence
   • प्रशिक्षण युगों की संख्या
   • 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