Understanding and modeling collective intelligence is essential for addressing complex social systems. Directed graphs called fuzzy cognitive maps (FCMs) offer a powerful tool for encoding causal mental models, but extracting high-integrity FCMs from text is challenging. This study presents an approach using large language models (LLMs) to automate FCM extraction. We introduce novel graph-based similarity measures and evaluate them by correlating their outputs with human judgments through the Elo rating system. Results show positive correlations with human evaluations, but even the best-performing measure exhibits limitations in capturing FCM nuances. Fine-tuning LLMs improves performance, but existing measures still fall short. This study highlights the need for soft similarity measures tailored to FCM extraction, advancing collective intelligence modeling with NLP.

本研究解决了从文本中提取高质量模糊认知图（FCMs）的挑战，通过利用大型语言模型（LLMs）自动化提取过程。研究提出了新颖的基于图的相似度测量方法，并通过与人类判断的关联评价其效果，结果表明这些方法与人类评价呈正相关，但依然存在局限性。这一研究推动了使用自然语言处理技术在集体智能建模上的进展。

提取因果集体智能的软度量方法