用大模型自动提取因果集体智能图，提升社会系统建模能力
Soft Measures for Extracting Causal Collective Intelligence
- 基于图结构设计新型相似性度量，结合大模型自动化提取因果图
- 与人类判断相关性显著，但现有方法仍难捕捉因果细节
- 适用于社会计算、认知建模研究者，推动人机协同决策研究
理解与建模集体智能对解决复杂社会系统至关重要。有向图形式的模糊认知图（FCMs）能有效编码因果心智模型，但从文本中提取高质量FCM仍具挑战。本研究提出利用大语言模型（LLMs）自动化提取FCM的方法，引入新型基于图的相似性度量，并通过埃洛评分系统将输出与人类判断进行对比评估。结果表明，各项度量与人类评价存在正相关，但最优方法仍难以完整捕捉FCM的细微差异。微调LLMs可提升性能，但现有度量仍不充分。研究强调需开发专用于FCM提取的软性相似性度量，推动基于自然语言处理的集体智能建模发展。
原文摘要 · Abstract (English)
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.
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