LLM Factoscope: Uncovering LLMs' Factual Discernment through Inner States Analysis
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:TZPP3FBVrecord.jsonopen to challenge →
read the original abstract
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.
This paper has not been read by Pith yet.
Forward citations
Cited by 2 Pith papers
-
From Retinal Evidence to Safe Decisions: RETINA-SAFE and ECRT for Hallucination Risk Triage in Medical LLMs
RETINA-SAFE benchmark and ECRT two-stage triage improve hallucination risk detection in medical LLMs for retinal decisions by 0.15-0.19 balanced accuracy over baselines using internal representations and logit shifts.
-
The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models
The paper introduces a four-source uncertainty taxonomy for LLMs and finds that consensus-based UQ methods outperform others while larger models show lower uncertainty estimates.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.