1Cademy - A language model is calculating the next tokens probability distribution over a set of four candidate tokens. The raw output scores (logits) for these tokens are: {Token A: 4.0, Token B: 3.8, Token C: 1.5, Token D: 1.2}. The current generation process uses a temperature parameter `β = 1.0`. A developer wants to modify the process to make the models output less predictable and increase the likelihood of selecting Token B relative to Token A. Which of the following adjustments to the temperature parameter `β` would best achieve this goal?

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Temperature-Scaled Softmax for Renormalized Probability

Multiple Choice

A language model is calculating the next token's probability distribution over a set of four candidate tokens. The raw output scores (logits) for these tokens are: {Token A: 4.0, Token B: 3.8, Token C: 1.5, Token D: 1.2}. The current generation process uses a temperature parameter β = 1.0. A developer wants to modify the process to make the model's output less predictable and increase the likelihood of selecting Token B relative to Token A. Which of the following adjustments to the temperature parameter β would best achieve this goal?

Updated 2025-09-28

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