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A Categorical Analysis of Large Language Models and Why LLMs Circumvent the Symbol Grounding Problem
Published: December 9, 2025 |
arXiv ID: 2512.09117v1
By: Luciano Floridi, Yiyang Jia, Fernando Tohmé
Potential Business Impact:
AI doesn't truly understand, it just tricks us.
Business Areas:
Natural Language Processing
Artificial Intelligence, Data and Analytics, Software
This paper presents a formal, categorical framework for analysing how humans and large language models (LLMs) transform content into truth-evaluated propositions about a state space of possible worlds W , in order to argue that LLMs do not solve but circumvent the symbol grounding problem.
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Country of Origin
🇺🇸
United States
Page Count
25 pages
Category
Computer Science:
Artificial Intelligence