Explanation:

Repetition penalty (also called presence penalty in some models) is specifically designed to reduce repetitive text generation.

• Option B is correct: Repetition penalty penalizes tokens that have already appeared in the generated text, making the model less likely to repeat the same phrases.
• Option A (Temperature) controls randomness but doesn't specifically address repetition issues. While adjusting temperature might help, it's not the targeted solution for repetition problems.

How it works:

1. The model tracks which tokens have already been generated
2. When repetition penalty is applied, the probability of already-generated tokens is reduced
3. This encourages the model to generate more diverse content

For phrases like "Thank you for using our service" that keep repeating, increasing the repetition penalty value would be the appropriate solution to make the output less repetitive.