Reduce the temperature setting is the correct inference setting to adjust to increase response determinism for repeated prompts because temperature controls the sampling randomness and lowering it makes the model favor higher probability tokens so outputs become more stable across identical inputs.

Lowering the temperature moves the model toward greedy or high probability token selection and reduces variation in responses. For a simple classification task like labeling comments as positive neutral or negative this makes the model more likely to return the same label each time the same prompt is submitted, and it affects token selection rather than output length.

Increase the temperature setting is incorrect because raising temperature injects more randomness into token choice and it will make repeated outputs less consistent rather than more consistent.

Raise the top-p value is incorrect because increasing top-p enlarges the candidate token pool for sampling and can increase variability, so it does not improve determinism when you need repeatable labels.

Increase the maximum generation length is incorrect because changing the allowed token length only affects how long the output can be and it does not reduce sampling randomness, so it will not make short classification labels more repeatable.

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