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Automating Psychological Hypothesis Generation with AI: Large Language Models Meet Causal Graph

2024-02-22 10:12:16
Song Tong, Kai Mao, Zhen Huang, Yukun Zhao, Kaiping Peng

Abstract

Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on `well-being', then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34, p=0.007 and t(59) = 4.32, p<0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.

Abstract (translated)

通过利用因果知识图和大型语言模型(LLM)之间的协同作用,我们的研究在心理学中引入了一种 groundbreaking 的计算假设生成方法。我们使用 LLM 对 43,312 篇心理学文章进行了分析,以提取因果关系对。这个分析产生了一个专门的心理学因果图。应用链接预测算法,我们生成了 130 个关注 `幸福` 的潜在心理假设,然后将这些假设与博士学者的研究想法以及仅由 LLM 生成的假设进行了比较。有趣的是,LLM 和因果图的结合方法在新颖性方面与专家水平见解相呼应,明显超越了 LLM 单独的假设(t(59) = 3.34,p = 0.007 和 t(59) = 4.32,p <0.001)。这种一致性通过深度语义分析得到了进一步证实。我们的结果表明,将 LLM 与机器学习技术(如因果知识图)相结合可以彻底颠覆自动发现心理学,从丰富的文献中提取新颖的见解。这项工作站在心理学和人工智能的十字路口,为心理学研究中的数据驱动假设生成树立了一个新的丰富的范式。

URL

https://arxiv.org/abs/2402.14424

PDF

https://arxiv.org/pdf/2402.14424.pdf


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