Idanwo Idiyele Pq-ti-ero

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. What does chain-of-thought prompting elicit in the cited worked-solution research?
   • A guarantee that every answer is logically correct
   • Only an unchanged database lookup identifier
   • Intermediate generated steps before a final answer
   • Automatic removal of all answer text
2. Gbolohun ti o rọrun wo ni o han lati ṣe okunfa ero-igbesẹ-igbesẹ laisi awọn apẹẹrẹ (odo-shot CoT)?
   • 'Dahun lẹsẹkẹsẹ'
   • 'Ṣe kukuru'
   • 'Foju ibeere naa'
   • 'Jẹ ki a ronu igbese nipa igbese'
3. What scope applies to the original paper’s findings about scale and CoT gains?
   • They prove every small model can never reason
   • They concern the tasks and model sizes actually evaluated
   • They prove every larger model must improve on every task
   • They establish that prompting changes model weights immediately
4. What does the cited self-consistency method aggregate after sampling different reasoning paths?
   • Their final answers
   • Only the alphabetical order of explanation words
   • Only one required greedy path
   • New model weights computed from each sampled answer
5. How does the original DeepSeek-R1 report’s training study differ from supplying a fixed model with CoT prompt examples?
   • It proves all displayed reasoning is a faithful internal trace
   • It requires no model training of any kind
   • It changes every prompt into a permanent tokenizer entry
   • It trains reasoning behavior through RL and a stated staged pipeline
6. In an autoregressive generated solution, how can an earlier step affect later generation?
   • It guarantees every later inference is correct
   • Its text can become context for subsequent tokens
   • It removes all attention to the original problem
   • It necessarily changes the model’s original weights
7. What feature distinguishes few-shot CoT exemplars from answer-only exemplars in the original prompting comparison?
   • Intermediate solution steps are included with the example answers
   • Every example omits its question
   • The prompt must retrain the model weights
   • All exemplars are only random strings with no answers
8. What did the cited unfaithful-explanation study show about some generated reasoning explanations?
   • They always reveal every true causal influence on the model
   • They prove every generated answer is false
   • They can omit biasing input influences while rationalizing the affected answer
   • They eliminate the need for external task verification