LLMs是内向者
LLMs are Introvert
AI总结：
本研究针对LLM模拟信息传播时缺乏心理真实性的问题，提出基于社会信息处理的思想链（SIP-CoT）机制并融合情感引导记忆，使智能体更接近真实人类互动，提升社会智能与模拟真实性。
摘要：
社交媒体和生成式人工智能的指数级增长已经改变了信息传播的方式，促进了连接性，但也加速了错误信息的扩散。理解信息传播动态并制定有效的控制策略对于减轻有害内容至关重要。传统模型，如SIR模型，提供了基本见解，但不足以捕捉在线互动的复杂性。先进方法，包括注意力机制和图神经网络，提高了准确性，但通常忽视了用户心理和行为动态。大型语言模型（LLMs）凭借其类人推理能力，为模拟信息传播的心理方面提供了新的潜力。我们引入了一个基于LLM的模拟环境，捕捉智能体不断变化的态度、情绪和反应。然而，初步实验揭示了LLM生成的行为与真实人类动态之间存在显著差距，特别是在立场检测和心理真实性方面。通过社会信息处理理论进行的详细评估识别出目标设定和反馈评估中的主要差异，这些差异源于标准LLM训练中缺乏情感处理。为了解决这些问题，我们提出了基于社会信息处理的思想链（SIP-CoT）机制，并通过情感引导记忆加以增强。该方法改善了对社会线索的解释、目标的个性化以及反馈的评估。实验结果证实，增强SIP-CoT的LLM智能体能更有效地处理社会信息，表现出更接近真实人类互动的行为、态度和情绪。总之，这项研究突出了当前基于LLM的传播模拟中的关键局限性，并展示了整合SIP-CoT和情感记忆如何显著增强LLM智能体的社会智能和真实性。
英文摘要：
The exponential growth of social media and generative AI has transformed information dissemination, fostering connectivity but also accelerating the spread of misinformation. Understanding information propagation dynamics and developing effective control strategies is essential to mitigate harmful content. Traditional models, such as SIR, provide basic insights but inadequately capture the complexities of online interactions. Advanced methods, including attention mechanisms and graph neural networks, enhance accuracy but typically overlook user psychology and behavioral dynamics. Large language models (LLMs), with their human-like reasoning, offer new potential for simulating psychological aspects of information spread. We introduce an LLM-based simulation environment capturing agents' evolving attitudes, emotions, and responses. Initial experiments, however, revealed significant gaps between LLM-generated behaviors and authentic human dynamics, especially in stance detection and psychological realism. A detailed evaluation through Social Information Processing Theory identified major discrepancies in goal-setting and feedback evaluation, stemming from the lack of emotional processing in standard LLM training. To address these issues, we propose the Social Information Processing-based Chain of Thought (SIP-CoT) mechanism enhanced by emotion-guided memory. This method improves the interpretation of social cues, personalization of goals, and evaluation of feedback. Experimental results confirm that SIP-CoT-enhanced LLM agents more effectively process social information, demonstrating behaviors, attitudes, and emotions closer to real human interactions. In summary, this research highlights critical limitations in current LLM-based propagation simulations and demonstrates how integrating SIP-CoT and emotional memory significantly enhances the social intelligence and realism of LLM agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
