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Large Language Models (LLMs) have revolutionized various domains withextensive knowledge and creative capabilities. However, a critical issue withLLMs is their tendency to produce outputs that diverge from factual reality.This phenomenon is particularly concerning in sensitive applications such asmedical consultation and legal advice, where accuracy is paramount. In thispaper, we introduce the LLM factoscope, a novel Siamese network-based modelthat leverages the inner states of LLMs for factual detection. Ourinvestigation reveals distinguishable patterns in LLMs' inner states whengenerating factual versus non-factual content. We demonstrate the LLMfactoscope'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 intoLLMs' inner workings for enhanced reliability and transparency.

LLM Factoscope: Uncovering LLMs' Factual Discernment through Inner States Analysis