We introduce CONA, a novel context-aware instruction paradigm for effective knowledge dissemination using generative pre-trained transformer (GPT) models. CONA is a flexible framework designed to leverage the capabilities of Large Language Models (LLMs) and incorporate DIKW (Data, Information, Knowledge, Wisdom) hierarchy to automatically instruct and optimise presentation content, anticipate potential audience inquiries, and provide context-aware answers that adaptive to the knowledge level of the audience group. The unique aspect of the CONA paradigm lies in its combination of an independent advisory mechanism and a recursive feedback loop rooted on the DIKW hierarchy. This synergy significantly enhances context-aware contents, ensuring they are accessible and easily comprehended by the audience. This paradigm is an early pioneer to explore new methods for knowledge dissemination and communication in the LLM era, offering effective support for everyday knowledge sharing scenarios. We conduct experiments on a range of audience roles, along with materials from various disciplines using GPT4. Both quantitative and qualitative results demonstrated that the proposed CONA paradigm achieved remarkable performance compared to the outputs guided by conventional prompt engineering.

翻译：我们提出CONA——一种利用生成式预训练Transformer（GPT）模型实现有效知识传播的新型上下文感知指令范式。CONA是一个灵活框架，旨在发挥大型语言模型（LLM）的能力，同时融合DIKW（数据、信息、知识、智慧）层级结构，自动指导和优化展示内容、预判受众潜在提问，并提供适应受众群体知识水平的上下文感知回答。该范式的独特之处在于其将独立咨询机制与基于DIKW层级的递归反馈回路相结合，这种协同显著增强了上下文感知内容，确保其易于受众获取和理解。该范式是LLM时代探索知识传播与沟通新方法的早期先驱，为日常知识共享场景提供了有效支撑。我们以GPT4为工具，针对一系列受众角色及各学科材料开展实验。定量与定性结果均表明：与传统提示工程引导的输出相比，所提出的CONA范式取得了显著更优的性能。