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

• URL: http://arxiv.org/abs/2504.12187v1
• Date: Wed, 16 Apr 2025 15:42:33 GMT
• Title: What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structure
• Authors: Céline Budding,
• Abstract summary: It is sometimes assumed that Large Language Models (LLMs) know language.<n>I argue that LLMs can acquire tacit knowledge as defined by Martin Davies (1990).
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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