Self-Consistency Prompting: Improving Accuracy

Self-consistency prompting generates multiple reasoning paths for the same problem, then selects the answer that appears most frequently. This technique improves accuracy by leveraging the model's ability to find correct answers through different reasoning approaches.

How Self-Consistency Works

Instead of asking once and accepting the answer, you generate multiple responses with chain-of-thought reasoning. Each response may use a different reasoning path, but correct answers tend to appear consistently. You select the answer that appears most often.

This works because while reasoning paths vary, correct answers are consistent. Incorrect answers are more random, so they appear less frequently. By selecting the most common answer, you're likely choosing the correct one.

When to Use Self-Consistency

Self-consistency is most valuable for problems where accuracy is critical: complex calculations, logical reasoning, analysis tasks, and any problem where you need high confidence in the answer.

The technique requires multiple generations, so it's slower and uses more tokens. Use it when accuracy matters more than speed or cost. For simple tasks, single-generation approaches are more efficient.

Implementation

Generate 5-10 responses with chain-of-thought reasoning. Extract the final answer from each response. Count how many times each answer appears. Select the most frequent answer as your result.

You can also analyze the reasoning paths. If multiple paths lead to the same answer through different logic, that increases confidence. If paths disagree, you may need to investigate further or refine your prompt.