  Large Language Models (LLMs) have revolutionized various domains with
extensive knowledge and creative capabilities. However, a critical issue with
LLMs is their tendency to produce outputs that diverge from factual reality.
This phenomenon is particularly concerning in sensitive applications such as
medical consultation and legal advice, where accuracy is paramount. In this
paper, we introduce the LLM factoscope, a novel Siamese network-based model
that leverages the inner states of LLMs for factual detection. Our
investigation reveals distinguishable patterns in LLMs' inner states when
generating factual versus non-factual content. We demonstrate the LLM
factoscope's effectiveness across various architectures, achieving over 96%
accuracy in factual detection. Our work opens a new avenue for utilizing LLMs'
inner states for factual detection and encourages further exploration into
LLMs' inner workings for enhanced reliability and transparency.
