{"id":1590,"date":"2026-07-21T08:08:26","date_gmt":"2026-07-21T08:08:26","guid":{"rendered":"https:\/\/www.visible-language.org\/journal\/?p=1590"},"modified":"2026-07-21T09:08:15","modified_gmt":"2026-07-21T09:08:15","slug":"issue-60-2-large-language-model","status":"publish","type":"post","link":"https:\/\/www.visible-language.org\/journal\/issue-60-2-large-language-model\/","title":{"rendered":"The Concept of a Large Language Model: Misconceptions About and Beneficial Conceptualizations of LLM Technology"},"content":{"rendered":"
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Issue 60.2<\/h5>\n

The Chocolate and Oyster Flavored Donut Hole Of Unoriginality<\/a><\/p>\n

Graphic Neutralization and Gender Stereotypes in Early Childhood: How Visual Variables Shape Children\u2019s Interpretations<\/a><\/p>\n

Illustrated Letterforms: A Taxonomy of Graphemic Construction<\/a><\/p>\n

Are You Sure? Exploring the Challenges and Opportunities of Using Generative AI in Communication Design Projects: A Cross\u2011Cultural Case Study of Student Reflections, Bias, and Critical Engagement<\/a><\/p>\n

The Concept of a Large Language Model: Misconceptions About and Beneficial Conceptualizations of LLM Technology<\/a><\/p>\n

Download Issue 60.2 \u27a4<\/a><\/p>\n<\/div>\n

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The Concept of a Large Language Model: Misconceptions About and Beneficial Conceptualizations of LLM Technology<\/h1>\n

Matthew Petersona<\/sup>, Colleen Pattonb<\/sup>, Michael Greenc<\/sup>, and Brayan D\u00edazd<\/sup><\/h3>\n

a<\/strong> Department of Graphic Design and Industrial Design, North Carolina State University, Raleigh, NC, USA; b<\/strong> Department of Psychology, North Carolina State University, Raleigh, NC, USA; c<\/strong> Laboratory for Analytic Sciences, North Carolina State University, Raleigh, NC, USA; d<\/strong> School of Teacher Education and Leadership, Utah State University, Logan, Utah, USA
Corresponding author: Matthew Peterson (mopeters[at]ncsu.edu)<\/span><\/h4>\n
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Abstract:<\/span> Large language models (LLMs) are transforming writing and planning practices across the breadth of knowledge work, where misconceptions about the technology abound. We contend that these misconceptions are rooted in the dispersal of disciplinary knowledge\u2014that multiple disciplines are needed to understand LLMs. We bring together disciplinary knowledge to address four constructs commonly applied to LLMs: writing, learning, hallucination, and thinking. Misconceptions about LLM technology are embodied in and transmitted through conceptual metaphors and analogies. We thus provide a basic description of LLM technology with a careful accounting for metaphor. This description and the discussion of the four constructs leads to corrected statements for understanding LLMs. For instance, the assertion that \u201cLLMs think\u201d is replaced with: \u201cLLMs produce outputs that resemble the products of thinking.\u201d Finally, we consider common and potential metaphors for more accurately conceptualizing LLMs, and favor LLMs as answer simulators, remixers, fuzzy thesauruses, and plagiarism traps. The statements and metaphors provided here are a starting point for well-grounded usage patterns with LLMs for professionals, researchers, and students alike.<\/p>\n<\/div>\n

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Keywords:<\/span> analogical reasoning; conceptual metaphor theory; large language models; LLM hallucination; metaphor; multidisciplinarity; semiotics; writing<\/p>\n<\/div>\n

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