May 22, 2024Open Access

Large Language Models are Effective Priors for Causal Graph Discovery

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Causal graph discovery improves via large language models' prior information—enhancing structure assessments.
Using common-sense benchmarks, the methodology finds marked improvements, especially for edge directionality.
Causal graph prompting designs were systematically studied, proving their effectiveness in refining hypotheses rapidly and affordably. Systematic assessment reveals both the potential and limitations of applying LLMs in causal exploration, indicating areas for future refinement.

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Methodology integrates large language models to enhance causal graph discovery, suggesting significant implications for modeling.

Darvariu et al. (2024) studied this question.

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