Self-Consistency

Self-consistency is an advanced prompting technique where multiple responses are generated for the same prompt, and the most consistent or frequent answer is selected as the final output. This approach leverages the probabilistic nature of large language models, which can produce different outputs for the same prompt due to inherent randomness or ambiguity in the task.

By aggregating several outputs and choosing the most common or consistent answer, self-consistency reduces the impact of random errors, increases reliability, and helps ensure that the final response is robust and trustworthy. This technique is especially valuable for high-stakes, mission-critical, or ambiguous tasks where a single response may not be sufficient.

Key Characteristics

• Involves sampling multiple outputs (e.g., by running the same prompt several times)
• Aggregates or selects the most consistent answer, often using majority voting or consensus
• Reduces randomness and increases reliability of the final output
• Useful for tasks where accuracy, stability, or reproducibility are critical
• Can be automated (using scripts or APIs) or performed manually for smaller tasks
• Helps identify uncertainty or ambiguity in the model's responses

When to Use

• For tasks with high variability, uncertainty, or multiple plausible answers
• When you want to improve answer stability and reduce the risk of outlier responses
• For high-stakes, mission-critical, or regulated applications (e.g., healthcare, finance)
• When you need to filter out inconsistent, contradictory, or low-confidence outputs
• For research, benchmarking, or model evaluation

Strengths and Limitations

• Strengths:
  • Increases reliability and trustworthiness of outputs
  • Reduces the impact of random errors, model uncertainty, or prompt sensitivity
  • Helps surface uncertainty or ambiguity in the model's knowledge
  • Can be combined with other techniques (e.g., chain-of-thought) for even greater robustness
• Limitations:
  • Requires more computation, time, and resources (multiple runs per prompt)
  • May not resolve ambiguity if the prompt itself is unclear or poorly designed
  • Aggregation methods (e.g., majority voting) may not always select the best answer for subjective or open-ended tasks
  • Not always practical for real-time or low-latency applications

Example Prompt

• "What is the next number in the sequence: 2, 4, 6, 8, ...?"
• "Summarize the main point of this article."

Example Result

Most responses: 10
Final answer: 10

Best Practices

• Generate several outputs for the same prompt (e.g., 5-20 samples, depending on the task)
• Use majority voting, consensus, or other aggregation methods to select the final answer
• Useful for critical or high-stakes tasks where reliability is paramount
• Review the distribution of responses for insight into model uncertainty or ambiguity
• Combine with prompt refinement, chain-of-thought, or other advanced techniques for best results
• Automate the process for large-scale or production use cases
• Document the aggregation method and number of samples used for transparency