Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Integrating Local and Global Entropy for Uncertainty Quantification in LLMs
TL;DR
Large language models (LLMs) often produce incorrect outputs with high confidence, highlighting the need for better uncertainty quantification (UQ). The authors developed a method called Global-Local Uncertainty (GLU) that combines local token-level entropy and global hidden-state geometric entropy to assess uncertainty.
✦ Why It Matters
Engineers can implement GLU to improve uncertainty assessments in LLMs, enhancing model reliability in real-world applications.
Key Takeaways
- Integrate local and global entropy for better UQ in LLMs.
- Use GLU to identify confident-but-wrong outputs effectively.
- Apply a single forward pass for efficient uncertainty measurement.
- Test GLU across various model families for robust performance.
How It Works
GLU combines local token-level entropy with global geometric entropy from hidden states. By treating these two measures as near-orthogonal, it captures distinct failure modes, particularly the confident-but-wrong outputs that traditional methods miss.
The multiplicative gate mechanism allows for a seamless integration of these signals, enhancing the overall uncertainty quantification process.