Soft Measures for Extracting Causal Collective Intelligence

• URL: http://arxiv.org/abs/2409.18911v1
• Date: Fri, 27 Sep 2024 16:54:36 GMT
• Title: Soft Measures for Extracting Causal Collective Intelligence
• Authors: Maryam Berijanian, Spencer Dork, Kuldeep Singh, Michael Riley Millikan, Ashlin Riggs, Aadarsh Swaminathan, Sarah L. Gibbs, Scott E. Friedman, Nathan Brugnone,
• Abstract summary: Directed graphs called fuzzy cognitive maps (FCMs) offer a powerful tool for encoding causal mental models. 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.
• Score: 2.1948331804353223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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