The correct options are Temperature and Frequency penalty.

Lowering Temperature makes the model more deterministic and reduces the chance of unusual or highly varied word choices, so responses become less random and more focused.

Raising the Frequency penalty applies a cost to tokens that appear repeatedly in the generated text, so it directly discourages repeated words and phrases within a reply.

Top P sampling is a probabilistic sampling method that limits the cumulative probability of candidate tokens and it can affect randomness, but it is not the direct control for reducing repeated words in a reply.

Presence penalty encourages the model to introduce new tokens that have not appeared yet and it helps topic diversity, but it does not target the frequency of repeated tokens as directly as the frequency penalty.

Max tokens only limits the output length and it does not control randomness or repetition behavior within the allowed output.

Top K sampling restricts the candidate pool to the highest probability tokens and it influences randomness, but it is not the primary parameter to reduce repeated words compared with adjusting temperature and the frequency penalty.

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