  We propose semantic entropy probes (SEPs), a cheap and reliable method for
uncertainty quantification in Large Language Models (LLMs). Hallucinations,
which are plausible-sounding but factually incorrect and arbitrary model
generations, present a major challenge to the practical adoption of LLMs.
Recent work by Farquhar et al. (2024) proposes semantic entropy (SE), which can
detect hallucinations by estimating uncertainty in the space semantic meaning
for a set of model generations. However, the 5-to-10-fold increase in
computation cost associated with SE computation hinders practical adoption. To
address this, we propose SEPs, which directly approximate SE from the hidden
states of a single generation. SEPs are simple to train and do not require
sampling multiple model generations at test time, reducing the overhead of
semantic uncertainty quantification to almost zero. We show that SEPs retain
high performance for hallucination detection and generalize better to
out-of-distribution data than previous probing methods that directly predict
model accuracy. Our results across models and tasks suggest that model hidden
states capture SE, and our ablation studies give further insights into the
token positions and model layers for which this is the case.
