Pre-Generation Hallucination Detection in Large Language Models via Soft-Target Attention Probing
Detecting hallucination risk before generation enables abstention, retrieval augmentation, and routing decisions without incurring the cost of decoding. While prior work has shown that such risk can be estimated from a model's internal representations, existing approaches treat this as binary classification over a single decoded output. We instead formulate it as a risk-estimation problem. Under this formulation, we introduce soft-target supervision based on the empirical answer error rate over stochastically sampled outputs - an estimator we prove to be the unique unbiased minimum-variance estimator of the model's per-prompt error probability under its sampling distribution. We further a...
| Authors | Amina Miftakhova, Alexey Zaytsev |
|---|---|
| Primary category | cs.CL |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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