A Categorical Analysis of Large Language Models and Why LLMs Circumvent the Symbol Grounding Problem
This addresses a foundational issue in AI and philosophy of mind regarding meaning and truth for researchers and theorists, but it is incremental as it builds on existing debates without introducing new empirical methods.
The paper tackles the problem of how large language models (LLMs) handle truth and meaning by proposing a categorical framework to analyze content transformation into propositions, concluding that LLMs circumvent rather than solve the symbol grounding problem.
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.