LLM Factoscope: Uncovering LLMs' Factual Discernment through Inner States Analysis
This addresses accuracy issues in sensitive applications like medical and legal advice, representing a novel method for a known bottleneck.
The paper tackled the problem of LLMs generating non-factual content by analyzing their inner states, achieving over 96% accuracy in factual detection across various architectures.
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.