从分歧到共识：评估大语言模型在通过自适应策略促进达成一致中的作用
From Divergence to Consensus: Evaluating the Role of Large Language Models in Facilitating Agreement through Adaptive Strategies
AI总结：
本研究提出以大语言模型为自动化协调者的群体决策共识框架，结合自适应策略迭代优化提案，实验验证ChatGPT 4.0表现最优，为提升集体决策效率提供新路径。
摘要：
群体决策中达成共识通常需要克服诸多重大挑战，尤其是在调和多元观点、减轻阻碍达成一致的偏见方面。依赖人工协调者的传统方法往往受限于可扩展性和效率，在大规模、快节奏的讨论中尤为明显。为应对这些挑战，本研究提出一个新颖框架，在定制化多用户聊天系统中采用大语言模型（LLM）作为自动化协调者。该方法以余弦相似度为核心指标，评估ChatGPT 4.0、Mistral Large 2和AI21 Jamba Instruct这三款前沿LLM生成契合参与者观点的共识提案的能力。与传统技术不同，该系统整合了自适应协调策略，包括澄清误解、总结讨论、提出折中方案，使LLM能够根据用户反馈迭代优化共识提案。实验结果证明ChatGPT 4.0表现更优，与参与者观点的契合度更高，与同类模型相比达成共识所需迭代次数更少。此外，分析揭示了这些模型在气候行动、优质教育、良好健康与福祉、清洁饮用水和环境卫生设施获取等各类可持续发展主题讨论中的差异化表现。这些发现凸显了LLM驱动的协调对优化集体决策过程的变革性潜力，也强调了未来研究中完善评估指标与跨文化适应性的重要性。
英文摘要：
Achieving consensus in group decision-making often involves overcoming significant challenges, particularly in reconciling diverse perspectives and mitigating biases that hinder agreement. Traditional methods relying on human facilitators are often constrained by scalability and efficiency, especially in large-scale, fast-paced discussions. To address these challenges, this study proposes a novel framework employing large language models (LLMs) as automated facilitators within a custom-built multi-user chat system. Leveraging cosine similarity as a core metric, this approach evaluates the ability of three state-of-the-art LLMs- ChatGPT 4.0, Mistral Large 2, and AI21 Jamba Instruct- to synthesize consensus proposals that align with participants' viewpoints. Unlike conventional techniques, the system integrates adaptive facilitation strategies, including clarifying misunderstandings, summarizing discussions, and proposing compromises, enabling the LLMs to iteratively refine consensus proposals based on user feedback. Experimental results demonstrate the superiority of ChatGPT 4.0, which achieves higher alignment with participant opinions, requiring fewer iterations to reach consensus compared to its counterparts. Moreover, analysis reveals the nuanced performance of the models across various sustainability-focused discussion topics, such as climate action, quality education, good health and well-being, and access to clean water and sanitation. These findings highlight the transformative potential of LLM-driven facilitation for improving collective decision-making processes and underscore the importance of advancing evaluation metrics and cross-cultural adaptability in future research.
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。
