Title: Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations Show Chinese title

Title: 共情提示：多模态大语言模型对话中的非语言上下文整合

Abstract: We present Empathic Prompting, a novel framework for multimodal human-AI interaction that enriches Large Language Model (LLM) conversations with implicit non-verbal context. The system integrates a commercial facial expression recognition service to capture users' emotional cues and embeds them as contextual signals during prompting. Unlike traditional multimodal interfaces, empathic prompting requires no explicit user control; instead, it unobtrusively augments textual input with affective information for conversational and smoothness alignment. The architecture is modular and scalable, allowing integration of additional non-verbal modules. We describe the system design, implemented through a locally deployed DeepSeek instance, and report a preliminary service and usability evaluation (N=5). Results show consistent integration of non-verbal input into coherent LLM outputs, with participants highlighting conversational fluidity. Beyond this proof of concept, empathic prompting points to applications in chatbot-mediated communication, particularly in domains like healthcare or education, where users' emotional signals are critical yet often opaque in verbal exchanges. Show Chinese abstract

Abstract: 我们提出了共情提示，这是一种用于多模态人机交互的新框架，通过隐含的非语言上下文来丰富大型语言模型（LLM）的对话。 该系统集成了一个商业面部表情识别服务，以捕捉用户的感情线索，并在提示过程中将其作为上下文信号嵌入。 与传统的多模态界面不同，共情提示不需要显式的用户控制；相反，它不显眼地将情感信息添加到文本输入中，以实现对话和流畅度的一致性。 该架构是模块化且可扩展的，允许集成额外的非语言模块。 我们描述了系统设计，通过本地部署的DeepSeek实例实现，并报告了一个初步的服务和可用性评估（N=5）。 结果表明，非语言输入被一致地整合到连贯的LLM输出中，参与者强调了对话的流畅性。 除了这个概念验证之外，共情提示还指向了聊天机器人中介通信的应用，特别是在医疗或教育等领域，其中用户的感情信号至关重要，但在口头交流中往往不透明。