Comparison
Self-Consistency vs Temperature
Self-Consistency and Temperature are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Self-Consistency
When the task has a verifiable answer (math, logic, code that compiles) and N× compute is acceptable.
A GSM8K eval: sample 32 CoT completions per problem, take the majority numeric answer.
When you would reach for Temperature
Low for code/extraction; medium for chat; high for creative writing.
Temperature 0: same prompt, same response, every time.
Frequently asked
What is the difference between Self-Consistency and Temperature?
Self-Consistency: Self-consistency samples N chain-of-thought completions for the same problem and takes the majority answer. Improves accuracy on math and reasoning tasks at N× the cost. Temperature: Temperature is a generation parameter that controls randomness. 0 is deterministic (always pick the most likely token); higher values produce more diverse, surprising output.
When should I use Self-Consistency vs Temperature?
When the task has a verifiable answer (math, logic, code that compiles) and N× compute is acceptable. Low for code/extraction; medium for chat; high for creative writing.
Are Self-Consistency and Temperature the same thing?
No. Self-Consistency is prompting; Temperature is inference. They are related but address different parts of the AI stack.