A Step Towards Modern Disinformation Detection: Novel Methods for Detecting LLM-Generated Text

New generative artificial intelligence (GenAI) technology could have devastating consequences on our democracy because it can be easily used to spread disinformation at scale while simultaneously personalizing propaganda to demographics or individuals. The threat is significant – Large-scale GenAI-based disinformation campaigns can sway public opinion, shape political events, or even compromise the integrity of elections. One method for defending against large-scale GenAI disinformation is to build tools for autonomously detecting AI-generated content. In this article, we evaluate state-of-the-art AI detection tools. Additionally, we propose a novel AI content detection method which demonstrates up to a 48% improvement in accuracy (over existing tools) for autonomously detecting AI-generated content.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/milcom61039.2024.10773838
Citations
4
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
1.39
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article

A Step Towards Modern Disinformation Detection: Novel Methods for Detecting LLM-Generated Text

David Na, Lanier Watkins, Yinzhi Cao, Samuel Nathanson et al.
4 citations
Natural Language Processing Techniques
1.39
article

A Step Towards Modern Disinformation Detection: Novel Methods for Detecting LLM-Generated Text

David Na, Lanier Watkins, Yinzhi Cao, Samuel Nathanson, Yungjun Yoo
article en
4 citations

Abstract

New generative artificial intelligence (GenAI) technology could have devastating consequences on our democracy because it can be easily used to spread disinformation at scale while simultaneously personalizing propaganda to demographics or individuals. The threat is significant – Large-scale GenAI-based disinformation campaigns can sway public opinion, shape political events, or even compromise the integrity of elections. One method for defending against large-scale GenAI disinformation is to build tools for autonomously detecting AI-generated content. In this article, we evaluate state-of-the-art AI detection tools. Additionally, we propose a novel AI content detection method which demonstrates up to a 48% improvement in accuracy (over existing tools) for autonomously detecting AI-generated content.

Johns Hopkins University (US)
Openalex Percentile: Top 15%
Natural Language Processing Techniques
1.39
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