Article
Version 2
Preserved in Portico This version is not peer-reviewed
Elucidating Common Fallacies and Misconceptions Around LLMs
Version 1
: Received: 23 November 2023 / Approved: 23 November 2023 / Online: 24 November 2023 (05:47:20 CET)
Version 2 : Received: 24 November 2023 / Approved: 24 November 2023 / Online: 25 November 2023 (14:29:21 CET)
Version 2 : Received: 24 November 2023 / Approved: 24 November 2023 / Online: 25 November 2023 (14:29:21 CET)
How to cite: Khan, M. A.-Z.; Innan, N.; Hammujuddy, J. Elucidating Common Fallacies and Misconceptions Around LLMs. Preprints 2023, 2023111559. https://doi.org/10.20944/preprints202311.1559.v2 Khan, M. A.-Z.; Innan, N.; Hammujuddy, J. Elucidating Common Fallacies and Misconceptions Around LLMs. Preprints 2023, 2023111559. https://doi.org/10.20944/preprints202311.1559.v2
Abstract
This paper discusses some of the most common misconceptions about large language models (LLMs), including the belief that they are sentient or conscious, that they are always accurate, and that they can replace human creativity. The paper also proposes a strategy for overcoming these misbeliefs, which involves educating the public about the capabilities and limitations of LLMs, developing guidelines for the responsible use of LLMs, and conducting more research to understand the potential impact of LLMs on society.
Keywords
Large Language Models, Artificial Intelligence
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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Commenter: Muhammad Al-Zafar Khan
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