Enhancing Collective Intelligence in Large Language Models Through Emotional Integration

• URL: http://arxiv.org/abs/2503.04849v1
• Date: Wed, 05 Mar 2025 23:42:48 GMT
• Title: Enhancing Collective Intelligence in Large Language Models Through Emotional Integration
• Authors: Likith Kadiyala, Ramteja Sajja, Yusuf Sermet, Ibrahim Demir,
• Abstract summary: This research investigates the integration of emotional diversity into Large Language Models (LLMs) to enhance collective intelligence.<n>Inspired by the human wisdom of crowds phenomenon, we fine-tuned the DarkIdol-Llama-3.1-8B model using Google's GoEmotions dataset and Low-Rank Adaptation (LoRA) to simulate emotionally diverse responses.
• Score: 0.2812395851874055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research investigates the integration of emotional diversity into Large Language Models (LLMs) to enhance collective intelligence. Inspired by the human wisdom of crowds phenomenon, where group decisions often outperform individual judgments, we fine-tuned the DarkIdol-Llama-3.1-8B model using Google's GoEmotions dataset and Low-Rank Adaptation (LoRA) to simulate emotionally diverse responses. Evaluating the model on a distance estimation task between Fargo, ND, and Seattle, WA, across 15,064 unique persona configurations, we analyzed how emotional states and social attributes influence decision-making. Our findings demonstrate that emotional integration shapes response patterns while maintaining acceptable prediction accuracy, revealing its potential to enhance artificial collective intelligence. This study provides valuable insights into the interplay of emotional diversity and decision-making in LLMs, suggesting pathways for creating emotionally aware AI systems that balance emotional depth with analytical precision.

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