Calibrated Entropy Score Detects LLM Hallucinations in One Pass
Hey, so there's this new method for spotting hallucinations in large language models that’s pretty cool. It only needs one pass through the model and works with black-box access to token logits. They call it the Calibrated Entropy Score (CES), and it looks at how entropy varies at the token level to detect hallucinations statistically. Unlike other techniques that require multiple passes or access to internal states, CES combines average and peak entropy signals using a calibrated distribution function. The research shows that the way entropy behaves can indicate if something is factually wrong. You can find it on arXiv with the number 2605.28264v1, and it really helps with trust issues in using LLMs in important settings.
Key facts
- CES requires only a single forward pass and black-box access to token logits.
- Hallucination detection is formalized as a statistical hypothesis test.
- Token-level entropy distribution shape and tail behavior indicate hallucinations.
- CES combines mean and maximum entropy signals via a calibrated reference CDF.
- Existing methods typically need multiple forward passes or model internals.
- The paper is published on arXiv with ID 2605.28264v1.
- LLMs often generate factually incorrect outputs undermining trust.
- High-stakes settings limit deployment due to hallucination risks.
Entities
Institutions
- arXiv