提出对话分析框架，揭示人机协作中深层互动机制
Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts
- 构建双层编码体系，融合认知与元认知调控机制
- 在九个跨领域数据集上验证框架有效性，发现元认知调节是深度协作关键
- 适用于评估人机及多智能体协作质量，适合研究协同决策者
我们提出一个用于分析协作式问题解决情境中对话的理论框架，重点关注人机及多智能体协作中涌现的动态交互。随着智能系统具备自主推理与策略协作能力，理解协作过程中的对话互动对优化和评估此类合作关系日益重要。该框架通过分层双层编码方案，整合认知与非认知问题解决过程以及元认知调控机制，弥补了现有分析方法的关键局限。我们在涵盖多个领域的九个数据集上验证了该框架的有效性与通用性，揭示了人类与智能体如何协调知识、技能与努力以解决复杂问题，尤其表明元认知调节可作为深层协作的重要判别指标。
原文摘要 · Abstract (English)
We present a conceptual framework for analyzing dialogue in collaborative problem-solving contexts, with an emphasis on the emerging dynamics of human-AI and multi-agent collaboration. As intelligent systems become active agents capable of autonomous reasoning and strategic cooperation, understanding the dialogic interaction during collaborative problem solving is increasingly important for optimizing and evaluating such partnerships. Our framework addresses key limitations in current analytical approaches through a hierarchical two-layer coding scheme that integrates cognitive and non-cognitive problem solving with metacognitive regulatory mechanisms. We demonstrate its effectiveness and generalizability across nine datasets spanning multiple domains, and provide insights into how humans and agents coordinate their knowledge, skills, and efforts to solve complex problems, showing in particular that metacognitive regulation can be an essential discriminator of deeper collaboration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。