Article
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Explaining Misinformation Detection Using Large Language Models
Version 1
: Received: 23 April 2024 / Approved: 23 April 2024 / Online: 23 April 2024 (12:05:46 CEST)
A peer-reviewed article of this Preprint also exists.
Pendyala, V.S.; Hall, C.E. Explaining Misinformation Detection Using Large Language Models. Electronics 2024, 13, 1673. Pendyala, V.S.; Hall, C.E. Explaining Misinformation Detection Using Large Language Models. Electronics 2024, 13, 1673.
Abstract
LLMs are a compressed repository of a vast corpus of valuable information on which they are trained. Therefore, this work hypothesizes that LLMs such as Llama, Orca, Falcon, and Mistral can be used for misinformation detection by making them cross-check new information with the repository on which they are trained. Accordingly, this paper describes the findings from the investigation of the abilities of LLMs in detecting misinformation on multiple datasets. The results are interpreted using explainable AI techniques such as LIME, SHAP, and Integrated Gradients. The LLMs themselves are also asked to explain their classification. These complementary approaches aid in better understanding the inner workings of misinformation detection using LLMs and lead to conclusions about their effectiveness at the task.
Keywords
Large Language Models; Natural Language Processing; Misinformation Containment; Explainable AI
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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