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A team notices that their LLM responses are too random and inconsistent. They want answers to be more deterministic and stable while still allowing slight variability. Which adjustment should they make?
Real Exam
Community
RRitesh
A
Increase the temperature to 1.2
B
Decrease the temperature to 0.2
C
Increase top-k to 200
D
Increase top-p to 0.95
Explanation:
Explanation:
Temperature is a parameter that controls the randomness of LLM responses:
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Lower temperature (e.g., 0.2): Makes outputs more deterministic and focused on the highest probability tokens, reducing randomness while still allowing slight variability
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Higher temperature (e.g., 1.2): Increases randomness and creativity, making outputs more diverse but less consistent
Why B is correct:
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Decreasing temperature to 0.2 reduces randomness while maintaining slight variability
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This makes responses more stable and deterministic, addressing the team's concern about inconsistent outputs
Why other options are incorrect:
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A (Increase temperature to 1.2): Would make responses even more random and inconsistent
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C (Increase top-k to 200): Top-k sampling limits choices to k most likely tokens; increasing it allows more options but doesn't directly address randomness control
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D (Increase top-p to 0.95): Top-p (nucleus sampling) selects from tokens whose cumulative probability exceeds p; increasing it includes more tokens but doesn't specifically increase determinism
Key Concept: Temperature is the primary parameter for controlling randomness vs. determinism in LLM outputs.
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