Explaining Misinformation Detection Using Large Language Models

Author:

Pendyala Vishnu S.1ORCID,Hall Christopher E.2

Affiliation:

1. Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA

2. Department of Computer Science, San Jose State University, San Jose, CA 95192, USA

Abstract

Large language models (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 Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (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. The methodology is generic and nothing specific is assumed for any of the LLMs, so the conclusions apply generally. Primarily, when it comes to misinformation detection, the experiments show that the LLMs are limited by the data on which they are trained.

Funder

San Jose State University

Publisher

MDPI AG

Reference31 articles.

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4. Pendyala, V.S., and Tabatabaii, F.S.A. (2023, January 5–6). Spectral analysis perspective of why misinformation containment is still an unsolved problem. Proceedings of the 2023 IEEE Conference on Artificial Intelligence (CAI), Santa Clara, CA, USA.

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