AWS Certified AI Practitioner AIF-C01 Practice Question
To get repeatable, highly consistent answers from a text foundation model, which adjustment to the temperature inference parameter should the developer make?
Increase the temperature so the model explores more token options.
Set the temperature close to 0 to minimize randomness.
Leave temperature unchanged and lower the max-tokens limit instead.
Raise the temperature above 1.5 to reduce variability.
Temperature controls how randomly a model samples from its probability distribution when choosing the next token. A low value (for example, 0 or 0.1) narrows sampling to the most likely tokens, producing deterministic, repeatable answers. Raising temperature has the opposite effect, making responses more diverse. Changing max-tokens affects length, not randomness, and temperature is unrelated to training hyper-parameters such as learning rate.
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What does the temperature parameter do in text generation models?
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What does the temperature parameter do in text generation models?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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