  Despite the many advances of Large Language Models (LLMs) and their
unprecedented rapid evolution, their impact and integration into every facet of
our daily lives is limited due to various reasons. One critical factor
hindering their widespread adoption is the occurrence of hallucinations, where
LLMs invent answers that sound realistic, yet drift away from factual truth. In
this paper, we present a novel method for detecting hallucinations in large
language models, which tackles a critical issue in the adoption of these models
in various real-world scenarios. Through extensive evaluations across multiple
datasets and LLMs, including Llama-2, we study the hallucination levels of
various recent LLMs and demonstrate the effectiveness of our method to
automatically detect them. Notably, we observe up to 62% hallucinations for
Llama-2 in a specific experiment, where our method achieves a Balanced Accuracy
(B-ACC) of 87%, all without relying on external knowledge.
