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Related Experiment Video

Updated: Jun 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Large language models (LLMs) and the institutionalization of misinformation.

Maryanne Garry¹, Way Ming Chan¹, Jeffrey Foster²

¹Psychology, The University of Waikato, Hamilton, New Zealand.

Trends in Cognitive Sciences

|October 11, 2024

Summary

Large language models (LLMs) spread convincing misinformation, potentially eroding our ability to discern truth from falsehood. This could undermine reality monitoring, impacting decision-making and societal trust.

Keywords:

LLM cognition misinformation reality monitoring

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Area of Science:

Psychological Science
Artificial Intelligence Ethics

Background:

Large language models (LLMs) like ChatGPT generate vast amounts of online information, blending truth with falsehood.
The persuasive nature of LLM-generated content, informed by psychological science, can lead users to accept misinformation as fact.

Purpose of the Study:

To examine the potential impact of LLM-generated misinformation on users' reality monitoring capabilities.
To explore the feedback loop where user-generated misinformation is adopted by emerging LLMs, exacerbating the problem.

Main Methods:

Analysis of LLM content generation techniques.
Review of psychological principles influencing belief formation.
Conceptual modeling of misinformation propagation through LLM networks.

Main Results:

LLMs can disseminate convincing misinformation at scale.
A feedback loop exists where misinformation can be amplified by LLMs.
Erosion of reality monitoring may occur, leading to reliance on false information.

Conclusions:

The proliferation of LLM-generated misinformation poses a significant threat to individual and societal functions.
Loss of reality monitoring can lead to flawed decision-making, decreased trust in institutions, and societal fragmentation.