Summary of Soft Measures For Extracting Causal Collective Intelligence, by Maryam Berijanian et al.
Soft Measures for Extracting Causal Collective Intelligence
by Maryam Berijanian, Spencer Dork, Kuldeep Singh, Michael Riley Millikan, Ashlin Riggs, Aadarsh Swaminathan, Sarah L. Gibbs, Scott E. Friedman, Nathan Brugnone
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach uses large language models (LLMs) to automate the extraction of fuzzy cognitive maps (FCMs) from text, enabling the encoding of causal mental models. The study introduces graph-based similarity measures and evaluates their outputs through the Elo rating system, demonstrating positive correlations with human judgments. While the best-performing measure shows promise, it still falls short in capturing FCM nuances. Fine-tuning LLMs improves performance, highlighting the need for soft similarity measures tailored to FCM extraction. This research advances collective intelligence modeling using NLP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a way to use large language models (LLMs) to understand how people think and make decisions. They created a new method for extracting information from text that helps us understand complex systems, like social networks. The team tested their approach by comparing it to human judgments and found that it works well. However, there’s still room for improvement. By refining the model, we can better understand how people think and make decisions. |
Keywords
* Artificial intelligence * Fine tuning * Nlp
