September 28, 2025Open Access

Mitigating Hallucinations in Large Language Models via Causal Reasoning

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Experimental analysis improves causal reasoning in large language models, suggesting a method to reduce hallucinations.

CDCR-SFT reduced hallucinations by 10% and achieved 95.33% accuracy on CLADDER, surpassing human performance.
Key result highlighted a significant inverse relationship between causal reasoning and hallucinations in large language models.
Causal-DAG construction and reasoning were utilized to enhance causal understanding in large language models' outputs.
Findings suggest that better causal modeling methods may mitigate logical inconsistencies in outputs of large language models.

Li et al. (2025) studied this question.

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