The Rise of GenAI and the Confidence Challenge
GenAI has revolutionized business efficiency with its rapid development, scalability, and maintainability advantages. However, generating reliable confidence scores remains a critical challenge, especially in financial applications where accuracy is paramount.
Exploring Three Key Approaches
Our research explored three distinct methods for generating confidence scores:
- Calibrator Models: Independent GenAI models evaluating other models’ outputs
- Logarithmic Probabilities (Logprobs): Token-based probability measurements
- Majority Voting: Ensemble method selecting the most common response
Majority Voting: The Winning Strategy
Among the three approaches, majority voting emerged as the most effective solution, demonstrating:
- Strong positive correlation with accuracy
- Consistent and interpretable results
- Flexible implementation options
Implementation Considerations
Successful implementation requires careful attention to:
- Optimal model count (4-7 models recommended)
- Weight assignment strategies
- Confidence score calibration using Platt scaling
Challenges and Limitations
Key challenges include:
- Handling long text fields effectively
- Addressing granularity issues in confidence scoring
- Balancing computational costs with accuracy requirements
Future Developments
While majority voting provides a solid foundation for confidence scoring in GenAI applications, ongoing research continues to explore more robust solutions for handling long text fields and improving granularity without sacrificing performance.
Read the complete case study on Spotify Engineering Blog for more detailed insights