  Concerns regarding the propensity of Large Language Models (LLMs) to produce
inaccurate outputs, also known as hallucinations, have escalated. Detecting
them is vital for ensuring the reliability of applications relying on
LLM-generated content. Current methods often demand substantial resources and
rely on extensive LLMs or employ supervised learning with multidimensional
features or intricate linguistic and semantic analyses difficult to reproduce
and largely depend on using the same LLM that hallucinated. This paper
introduces a supervised learning approach employing two simple classifiers
utilizing only four numerical features derived from tokens and vocabulary
probabilities obtained from other LLM evaluators, which are not necessarily the
same. The method yields promising results, surpassing state-of-the-art outcomes
in multiple tasks across three different benchmarks. Additionally, we provide a
comprehensive examination of the strengths and weaknesses of our approach,
highlighting the significance of the features utilized and the LLM employed as
an evaluator. We have released our code publicly at
https://github.com/Baylor-AI/HalluDetect.
