Consider two distinct scenarios for a language model's next-token prediction. In Scenario A, the probability distribution is highly 'peaked,' with the single most likely token having a probability of 0.9. In Scenario B, the distribution is 'flat,' with the 20 most likely tokens each having a probability of 0.04. For both scenarios, analyze how the size of the candidate token pool would differ between a sampling method with a fixed pool of the 10 most probable tokens and a method that selects from the smallest set of tokens whose cumulative probability exceeds 0.85. Discuss the likely impact of these differences on the diversity of the generated text in each case.