Elucidating Common Fallacies and Misconceptions Around LLMs

Muhammad Al-Zafar Khan; Nouhaila Innan; Jahvaid Hammujuddy

doi:10.20944/preprints202311.1559.v2

Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

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)

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.

Large Language Models, Artificial Intelligence

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Comment 1

Received: 25 November 2023
Commenter: Muhammad Al-Zafar Khan
Commenter's Conflict of Interests: Author

Comment: The incorrect LaTeX / TeX compressed folder was uploaded resulting in the incorrect paper being uploaded with the incorrect title.When compiled, please verify that the PDF generated from LaTeX / TeX file, and PDF file uploaded are the same.

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Elucidating Common Fallacies and Misconceptions Around LLMs

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