@inproceedings{he-etal-2024-llm,
title = "{LLM} Factoscope: Uncovering {LLM}s{'} Factual Discernment through Measuring Inner States",
author = "He, Jinwen and
Gong, Yujia and
Lin, Zijin and
Wei, Cheng{'}an and
Zhao, Yue and
Chen, Kai",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.608",
pages = "10218--10230",
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. Inspired by human lie detectors using physiological responses, 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 its effectiveness across various architectures, achieving over 96{\%} accuracy on our custom-collected factual detection dataset. 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.",
}
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<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. Inspired by human lie detectors using physiological responses, 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 its effectiveness across various architectures, achieving over 96% accuracy on our custom-collected factual detection dataset. 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.</abstract>
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%0 Conference Proceedings
%T LLM Factoscope: Uncovering LLMs’ Factual Discernment through Measuring Inner States
%A He, Jinwen
%A Gong, Yujia
%A Lin, Zijin
%A Wei, Cheng’an
%A Zhao, Yue
%A Chen, Kai
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F he-etal-2024-llm
%X 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. Inspired by human lie detectors using physiological responses, 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 its effectiveness across various architectures, achieving over 96% accuracy on our custom-collected factual detection dataset. 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.
%U https://aclanthology.org/2024.findings-acl.608
%P 10218-10230
Markdown (Informal)
[LLM Factoscope: Uncovering LLMs’ Factual Discernment through Measuring Inner States](https://aclanthology.org/2024.findings-acl.608) (He et al., Findings 2024)
ACL