Overview
The article discusses the creation of confidence scores for GenAI applications, particularly in financial automation. It evaluates three approaches—calibrator models, logarithmic probabilities, and majority voting—concluding that majority voting is the most effective method for generating reliable confidence scores.
What You'll Learn
1
How to implement majority voting for confidence scoring in GenAI applications
2
Why calibrator models may not be suitable for generating confidence scores
3
When to apply Platt scaling for confidence score calibration
Prerequisites & Requirements
- Understanding of GenAI and its applications in financial automation
- Experience with machine learning models and confidence scoring techniques(optional)
Key Questions Answered
What are the three approaches to generating confidence scores for GenAI?
The article discusses three approaches: calibrator models, logarithmic probabilities (logprobs), and majority voting. Majority voting was found to be the most effective method, demonstrating a strong correlation with accuracy in financial applications.
How does majority voting improve confidence scoring in GenAI applications?
Majority voting selects the most common response from multiple GenAI models, providing a confidence score based on the proportion of agreeing models. This method showed a strong positive correlation with accuracy in the tested financial automation application.
What challenges does majority voting face with long text fields?
Majority voting struggles with long text fields due to variations in phrasing, which can lead to disagreements among models. The article explores methods like embedding similarity and GenAI selection to address this issue, but they were ultimately not implemented due to their limitations.
What is Platt scaling and when should it be applied?
Platt scaling is a technique used to calibrate probabilistic outputs by adjusting raw confidence scores to better align with actual accuracy. It is applied when there is a need to correct over- or under-confidence in various fields of GenAI outputs.
Key Statistics & Figures
Majority voting accuracy for invoice total extraction
80%
This was determined by the proportion of agreeing models in the majority voting approach.
Confidence score accuracy after permutation approach
94%
33/35
Key Actionable Insights
1Implement majority voting as a confidence scoring method for GenAI applications to enhance accuracy.Majority voting has shown a strong correlation with accuracy in financial applications, making it a reliable choice for generating confidence scores.
2Consider using Platt scaling to calibrate confidence scores for better alignment with actual performance.This technique helps adjust the raw confidence scores, ensuring they reflect true accuracy levels, which is crucial in regulated environments.
3Evaluate the number of models in your ensemble carefully to balance stability and computational efficiency.Using too many similar models can lead to diminishing returns, while too few may lack stability. Aim for four to seven models for optimal performance.
Common Pitfalls
1
Relying solely on calibrator models can lead to inconsistent and difficult-to-interpret confidence scores.
This inconsistency is problematic in financial applications where clarity and reliability are essential for decision-making.
2
Using logprobs for confidence scoring may not yield reliable results due to lack of correlation with accuracy.
The article found that averaging logprobs did not correlate with actual performance, indicating that this method should be approached with caution.
Related Concepts
Genai Applications In Financial Automation
Confidence Scoring Techniques
Machine Learning Model Evaluation