用大模型当员工访谈代理，自动挖出组织里没人说出口的隐性知识。
Leveraging Large Language Models for Tacit Knowledge Discovery in Organizational Contexts
- 设计智能代理通过对话迭代重建数据描述，模拟知识传播过程。
- 在864次仿真中实现94.9%的知识召回率，无需直接接触领域专家。
- 适合需要挖掘组织隐性知识的企业或研究机构参考。
由于初始信息不全、难以识别专家、正式层级与非正式网络交织，以及提问策略困难，组织内隐性知识的记录极具挑战。为此，我们提出一种基于智能体的框架，利用大语言模型（LLM）通过与员工交互，迭代重构数据描述。将知识传播建模为感染力衰减的易感-感染（SI）过程，在多种合成公司结构和传播参数下进行864次仿真。结果表明，该智能体实现94.9%的完整知识召回率，自省反馈得分与外部文献评阅得分高度相关。分析发现，该方法可在不直接访问唯一领域专家的情况下恢复关键信息，凸显其在复杂组织环境中捕获碎片化知识的能力。
原文摘要 · Abstract (English)
Documenting tacit knowledge in organizations can be a challenging task due to incomplete initial information, difficulty in identifying knowledgeable individuals, the interplay of formal hierarchies and informal networks, and the need to ask the right questions. To address this, we propose an agent-based framework leveraging large language models (LLMs) to iteratively reconstruct dataset descriptions through interactions with employees. Modeling knowledge dissemination as a Susceptible-Infectious (SI) process with waning infectivity, we conduct 864 simulations across various synthetic company structures and different dissemination parameters. Our results show that the agent achieves 94.9% full-knowledge recall, with self-critical feedback scores strongly correlating with external literature critic scores. We analyze how each simulation parameter affects the knowledge retrieval process for the agent. In particular, we find that our approach is able to recover information without needing to access directly the only domain specialist. These findings highlight the agent's ability to navigate organizational complexity and capture fragmented knowledge that would otherwise remain inaccessible.
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