A Categorical Analysis of Large Language Models and Why LLMs Circumvent the Symbol Grounding Problem
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909953158021120 |
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| author | Floridi, Luciano Jia, Yiyang Tohmé, Fernando |
| author_facet | Floridi, Luciano Jia, Yiyang Tohmé, Fernando |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Categorical Analysis of Large Language Models and Why LLMs Circumvent the Symbol Grounding Problem Floridi, Luciano Jia, Yiyang Tohmé, Fernando Artificial Intelligence 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. |
| title | A Categorical Analysis of Large Language Models and Why LLMs Circumvent the Symbol Grounding Problem |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.09117 |