Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·14d ago·5 min read
CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders
TL;DR
CANDOR introduces a new discordance measure for evaluating frozen encoders, revealing that many encoders are not blind but rather weak in performance. This method corrects previous misconceptions about encoder capabilities across various datasets.
✦ Why It Matters
Use CANDOR to assess your frozen encoders' performance before training to identify weaknesses.
Key Takeaways
- Evaluate frozen encoders with CANDOR for better insights.
- Identify weak findings in encoders before training.
- Understand that high AUROC does not guarantee strong performance.
- Use equal-sized banks to ensure fair evaluation of models.
How It Works
CANDOR measures discordance by using equal-sized data banks that are symmetric under label swaps, ensuring a fixed chance level of 50%. This approach allows for a more accurate assessment of encoder performance, revealing weaknesses that traditional methods might overlook.