Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity
Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confidence score and abstains when confidence is low. Unsupervised sampling detectors (Semantic Entropy) avoid labels but plateau in quality, while supervised probes attain stronger in-distribution scores yet degrade sharply when calibration labels are scarce. We recover the response manifold of an LLM as the density ridge of a kernel density estimate built on a six-dimensional kinematic feature map of hidden state generation trajectories. A test generation is scored by the negated Euclidean distance from its projected feature point to the nearest...
| Authors | Nina I. Shamsi |
|---|---|
| Primary category | cs.LG |
| Published | 8 June 2026 |
| Last updated at source | 10 June 2026 |
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