Recent advances in artificial intelligence have seen Large Language Models (LLMs) demonstrate notable proficiency in causal discovery tasks. This study explores the factors influencing the performance of LLMs in causal discovery tasks. Utilizing open-source LLMs, we examine how the frequency of causal relations within their pre-training corpora affects their ability to accurately respond to causal discovery queries. Our findings reveal that a higher frequency of causal mentions correlates with better model performance, suggesting that extensive exposure to causal information during training enhances the models' causal discovery capabilities. Additionally, we investigate the impact of context on the validity of causal relations. Our results indicate that LLMs might exhibit divergent predictions for identical causal relations when presented in different contexts. This paper provides the first comprehensive analysis of how different factors contribute to LLM performance in causal discovery tasks.

翻译：人工智能领域的最新进展表明，大型语言模型（LLMs）在因果发现任务中展现出显著的能力。本研究探讨了影响LLMs在因果发现任务中性能的因素。通过使用开源LLMs，我们考察了预训练语料中因果关系的出现频率如何影响模型准确响应因果发现查询的能力。我们的研究结果表明，因果提及频率越高，模型性能越好，这表明在训练过程中广泛接触因果信息能够增强模型的因果发现能力。此外，我们还研究了上下文对因果关系有效性的影响。结果显示，当相同的因果关系出现在不同上下文中时，LLMs可能会给出不同的预测。本文首次全面分析了不同因素如何影响LLMs在因果发现任务中的性能。