What is Self-Consistency?
Self-consistency is a prompting technique that improves the reliability of AI reasoning by generating multiple responses to the same question and then selecting the most consistent answer through majority voting. Unlike traditional Chain-of-Thought prompting that uses greedy decoding for a single reasoning path, self-consistency leverages diverse sampling to explore multiple reasoning perspectives before converging on the most reliable answer.Why Use Self-Consistency?
- Improved Accuracy: Multiple samples reduce the impact of random errors and greedy decoding limitations
- Better Reasoning: Helps identify the most logical solution path from diverse perspectives
- Reduced Hallucinations: Inconsistent responses are filtered out through majority voting
- Confidence Assessment: Provides pseudo-probability likelihood of answer correctness
- Complex Problem Solving: Particularly effective for math, logic, and multi-step reasoning where single attempts may fail
- Robust Decision Making: Overcomes limitations of single reasoning paths in ambiguous scenarios
Basic Implementation in Latitude
Here’s a simple self-consistency example for classification tasks:Classification with Self-Consistency
How Self-Consistency Works
The self-consistency process follows three key steps:- Diverse Path Generation: The same prompt is submitted multiple times with higher temperature settings (0.6-0.8) to encourage different reasoning approaches and perspectives
- Answer Extraction: Each response is analyzed to extract the core answer or classification, regardless of the reasoning path taken
- Majority Voting: The most frequently occurring answer across all samples is selected as the final result
Advanced Implementation with Multiple Samples
Let’s create a more sophisticated example that uses Latitude’s chain feature to generate and compare multiple reasoning paths:- Multiple Sampling: We generate three independent solutions with higher temperature for diversity
- Chain Processing: Each step builds on the previous ones for comparison
- Consistency Analysis: A final step evaluates and selects the best answer
- Confidence Assessment: The system provides a confidence level based on agreement
Logic and Reasoning Self-Consistency
Use self-consistency for complex logical problems:Multi-Agent Self-Consistency
Combine self-consistency with Latitude’s agent system for specialized reasoning:Best Practices for Self-Consistency
Sample Generation
Sample Generation
Optimal Sampling:
- Use 3-5 samples for most problems (balance cost vs. accuracy)
- Increase temperature (0.6-0.8) to encourage diverse reasoning paths and overcome greedy decoding
- Ensure each sample approaches the problem independently
- Vary the prompt slightly to encourage different analytical perspectives
- Generate enough samples to identify patterns
- Filter out obviously flawed reasoning
- Weight samples based on reasoning quality, not just frequency
- Consider partial agreements in complex problems
Consistency Analysis
Consistency Analysis
Evaluation Criteria:
- Answer consistency: Do multiple samples reach the same conclusion?
- Reasoning quality: Which reasoning paths are most sound?
- Method diversity: Are different valid approaches represented?
- Confidence indicators: How certain can we be about the consensus?
- Majority voting for clear disagreements
- Weighted voting based on reasoning quality
- Partial credit for answers that are close but not identical
- Meta-reasoning about why inconsistencies occur
Problem Selection
Problem Selection
Best Use Cases:
- Classification tasks with potential ambiguity
- Mathematical word problems
- Logical reasoning puzzles
- Multi-step analytical tasks
- Questions with clear right/wrong answers where reasoning path matters
- Security-sensitive decisions requiring high confidence
- Creative writing tasks
- Subjective opinion questions
- Simple factual lookups
- Tasks requiring consistent style/voice
Performance Optimization
Performance Optimization
Efficiency Tips:
- Use parallel processing when possible
- Cache common problem types
- Implement early stopping if consensus is clear
- Balance sample count with accuracy needs
- Start with fewer samples and increase if needed based on consistency scores
- Use cheaper models for initial sampling, better models for final analysis
- Implement confidence thresholds to determine optimal sample count
- Consider the cost trade-off: higher accuracy vs. increased computational expense
- Remember that self-consistency has high costs but provides pseudo-probability confidence
Advanced Techniques
Adaptive Self-Consistency
Create prompts that adjust based on initial consistency. You can play with it hereNote how we used structured outputs to capture the consistency check results and decide whether to generate additional samples.
Self-Consistency with Uncertainty Quantification
Implement self-consistency that quantifies uncertainty:Integration with Other Techniques
Self-consistency works well combined with other prompting techniques:- Chain-of-Thought + Self-Consistency: Generate multiple detailed reasoning chains to overcome greedy decoding limitations
- Few-Shot + Self-Consistency: Use examples to guide consistent reasoning patterns across multiple samples
- Role-Playing + Self-Consistency: Have different expert personas solve the same problem independently
- Iterative Refinement + Self-Consistency: Use consensus to improve solution quality through multiple rounds
Related Techniques
Explore these complementary prompting techniques to enhance your AI applications:- Chain-of-Thought - Break down complex problems into step-by-step reasoning
- Tree-of-Thoughts - Explore multiple reasoning paths systematically
- Few-Shot Learning - Use examples to guide AI behavior and improve consistency