Automating Psychological Hypothesis Generation with AI: Large Language Models Meet Causal Graph
CoRR(2024)
摘要
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.
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