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.

翻译：本研究探讨将情感多样性整合到大型语言模型（LLMs）中以增强集体智能。受人类"群体智慧"现象启发——群体决策常优于个体判断，我们使用谷歌GoEmotions数据集和低秩自适应（LoRA）技术对DarkIdol-Llama-3.1-8B模型进行微调，以模拟情感多样化的响应。通过在15,064种独特角色配置上评估模型对北达科他州法戈市与华盛顿州西雅图市之间距离估算任务的表现，我们分析了情感状态与社会属性如何影响决策。研究结果表明，情感整合在保持可接受预测精度的同时塑造了响应模式，揭示了其增强人工集体智能的潜力。本研究为理解LLMs中情感多样性与决策机制的相互作用提供了重要见解，为创建兼具情感深度与分析精度、具有情感感知能力的人工智能系统指明了路径。