Simulation and the epistemology of transformer models

Operational similarity through sustained difference

Bibliographic Data

ID17675962
AuthorsDaniel Whelan-Shamy (0009-0005-2680-5434, Queensland University of Technology, corresponding author)
Year2026
Volume41
Issue7
Pages6589-6601
Publication date2026-04-15
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAI & Society (JOURNAL)
Journal identifiersISSN: 0951-5666 • E-ISSN: 1435-5655
PublisherSpringer Nature (PUBLISHER • SG)
DOI10.1007/s00146-026-03004-x
OpenAlexW7154498169
LanguageEN
References cited42

Extending simulation beyond its usual semiotic application, this article steps through the process by which Large Language Models (LLMs) reproduce similarity, to argue that simulation produces a “doubling” effect: an operational similarity sustained through difference. Accordingly, this article draws on literary theory to “read” transformer models with the intent of understanding how they operate as an infrastructure for the production of linguistic similarity. It is argued that neural networks enact an epistemology of exchange in which tokenisation, vectorisation, and self-attention render language commensurable, thereby producing an operational similarity that manifests differently in each natural language response generated. The political and epistemic implications of doubling are thereafter discussed with reference to the relationship between simulation, models and subjectivity

Computational linguistics · Natural language · Process (computing) · Semiotics · Similarity (geometry) · Transformer · Computational and Text Analysis Methods · Digital Humanities and Scholarship · Language and cultural evolution

Tools

Open DOIOpen Access