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cloud.google.com·1d ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Cross-Model Disagreement as a Label-Free Correctness Signal
TL;DR
Detecting errors in language models without relying on labeled data is challenging, especially when models are confidently wrong. Cross-Model Perplexity (CMP) and Cross-Model Entropy (CME) were developed to measure how surprised a second model is by the output of a first model, providing a label-free correctness signal.
✦ Why It Matters
Engineers can implement CMP and CME to improve error detection in language models without needing labeled data.
Key Takeaways
- Use Cross-Model Disagreement to assess model outputs without labels.
- Integrate CMP and CME into existing production systems easily.
- Leverage higher AUROC scores for better error detection.
- Apply findings to improve deployment monitoring and model routing.
How It Works
Cross-Model Disagreement operates by utilizing a second model to evaluate the output of a primary model. When the primary model generates an answer, the verifier model assesses how surprising or uncertain that answer is.
This is done through a single forward pass, eliminating the need for the verifier to generate its own outputs. The metrics CMP and CME quantify this surprise and uncertainty, respectively, providing a straightforward method for correctness estimation.