Words as Difference Makers: How Large Language Models Determine Causal Structure in Text
Because large language models (LLMs) are impressively successful in predicting text, it appears that they must have access to a 'world model' representing causal and definitional structure. However, the dominant formalisms of modern causal inference -- Judea Pearl's interventionist approach and the Neyman-Rubin potential outcomes framework -- struggle to illuminate how LLMs learn causal structure. I resolve this puzzle by arguing that LLMs employ a specific inductive approach based on a difference-making logic -- sometimes called variational induction. I demonstrate how central aspects of this logic are realized during training, where LLMs require enormous amounts of text data from a wide...
| Authors | Wolfgang Pietsch |
|---|---|
| Primary category | cs.CL |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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