Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Quantifying Uncertainty in AI Visibility: A Statistical Framework for Generative Search Measurement
TL;DR
Current methods for measuring visibility in AI search engines treat citation metrics as fixed values, ignoring their inherent variability. This study introduces a statistical framework that uses repeated sampling to analyze citation distributions across generative search platforms like OpenAI SearchGPT and Google Gemini.
✦ Why It Matters
Engineers can improve the accuracy of visibility metrics by incorporating uncertainty estimates into their analyses.
Key Takeaways
- Treat citation metrics as sample estimators, not fixed values.
- Use high-frequency sampling for more accurate visibility measurements.
- Report citation visibility with uncertainty estimates.
- Recognize the instability of citation rankings across different samples.
How It Works
The study employs two sampling regimes to capture citation variability, revealing that citation distributions often follow a power-law form. By using bootstrap confidence intervals, the authors assess the statistical significance of differences in citation visibility, highlighting the need for uncertainty estimates.