What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structure

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1. Verfasser: Budding, Céline
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Veröffentlicht: 2025
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author Budding, Céline
author_facet Budding, Céline
contents It is sometimes assumed that Large Language Models (LLMs) know language, or for example that they know that Paris is the capital of France. But what -- if anything -- do LLMs actually know? In this paper, I argue that LLMs can acquire tacit knowledge as defined by Martin Davies (1990). Whereas Davies himself denies that neural networks can acquire tacit knowledge, I demonstrate that certain architectural features of LLMs satisfy the constraints of semantic description, syntactic structure, and causal systematicity. Thus, tacit knowledge may serve as a conceptual framework for describing, explaining, and intervening on LLMs and their behavior.
format Preprint
id arxiv_https___arxiv_org_abs_2504_12187
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structure
Budding, Céline
Computation and Language
Artificial Intelligence
Machine Learning
It is sometimes assumed that Large Language Models (LLMs) know language, or for example that they know that Paris is the capital of France. But what -- if anything -- do LLMs actually know? In this paper, I argue that LLMs can acquire tacit knowledge as defined by Martin Davies (1990). Whereas Davies himself denies that neural networks can acquire tacit knowledge, I demonstrate that certain architectural features of LLMs satisfy the constraints of semantic description, syntactic structure, and causal systematicity. Thus, tacit knowledge may serve as a conceptual framework for describing, explaining, and intervening on LLMs and their behavior.
title What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structure
topic Computation and Language
Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2504.12187