用大模型同时扮演六顶思考帽，帮在线群体快速达成共识。
PTFA: An LLM-based Agent that Facilitates Online Consensus Building through Parallel Thinking
- 大模型并行执行六顶思考帽角色，自动处理文本输入。
- 试点验证可有效生成想法、探测情绪、分析观点质量。
- 适合需要高效协作的在线决策场景，如远程会议、政策研讨。
由于利益相关方意见多样，共识构建本身具有挑战性。有效的引导对推动共识形成和提升群体决策效率至关重要，但常受限于人类经验不足与扩展性差等问题。本文提出基于平行思维的引导代理PTFA，用于在线文本式共识构建。PTFA实时收集文本输入，并利用大语言模型（LLMs）并行执行广受认可的六顶思考帽技术中的六个独立角色。通过试点研究，验证了该代理在想法生成、情绪探查及观点质量深度分析方面的潜力。此外，识别出未来开放研究挑战，如调度优化与发散阶段行为管理。同时构建了一个综合性数据集，包含参与者间对话以及参与者与代理间的交互内容，供后续研究使用。
原文摘要 · Abstract (English)
Consensus building is inherently challenging due to the diverse opinions held by stakeholders. Effective facilitation is crucial to support the consensus building process and enable efficient group decision making. However, the effectiveness of facilitation is often constrained by human factors such as limited experience and scalability. In this research, we propose a Parallel Thinking-based Facilitation Agent (PTFA) that facilitates online, text-based consensus building processes.The PTFA automatically collects real-time textual input and leverages large language models (LLMs)to perform all six distinct roles of the well-established Six Thinking Hats technique in parallel thinking.To illustrate the potential of the agent, a pilot study was conducted, demonstrating its capabilities in idea generation, emotional probing, and deeper analysis of idea quality. Additionally, future open research challenges such as optimizing scheduling and managing behaviors in divergent phase are identified. Furthermore, a comprehensive dataset that contains not only the conversational content among the participants but also between the participants and the agent is constructed for future study.
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