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From task structures to world models: What do LLMs know?

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arxiv 2310.04276 v1 pith:E5EC66BL submitted 2023-10-06 cs.AI q-bio.NC

classification cs.AIq-bio.NC
keywords knowledgellmsmodelsworldanswerinstrumentaltaskwhat
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In what sense does a large language model have knowledge? The answer to this question extends beyond the capabilities of a particular AI system, and challenges our assumptions about the nature of knowledge and intelligence. We answer by granting LLMs "instrumental knowledge"; knowledge defined by a certain set of abilities. We then ask how such knowledge is related to the more ordinary, "worldly" knowledge exhibited by human agents, and explore this in terms of the degree to which instrumental knowledge can be said to incorporate the structured world models of cognitive science. We discuss ways LLMs could recover degrees of worldly knowledge, and suggest such recovery will be governed by an implicit, resource-rational tradeoff between world models and task demands.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Linear Spatial World Models Emerge in Large Language Models

    cs.AI 2025-06 reject novelty 5.0 of 10

    Spatial relation words in LLaMA and Qwen models form antipodal, roughly orthogonal directions in a low-dimensional subspace, and steering along these directions changes the model's output.

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