Comparison
Sampling vs Self-Consistency
Sampling and Self-Consistency are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Sampling
Sampling comes up when the question is fundamentally about inference.
OpenAI default: temperature 1.0, top-p 1.0.
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.
Frequently asked
What is the difference between Sampling and Self-Consistency?
Sampling: Sampling is the act of choosing the next token from the model's output distribution, typically after applying temperature and a truncation strategy like top-p or top-k. 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.
When should I use Sampling vs Self-Consistency?
Sampling is the right concept when you are focused on inference. When the task has a verifiable answer (math, logic, code that compiles) and N× compute is acceptable.
Are Sampling and Self-Consistency the same thing?
No. Sampling is inference; Self-Consistency is prompting. They are related but address different parts of the AI stack.