共情提示：多模态大语言模型对话中的非语言上下文整合
Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations
AI总结：
提出共情提示框架，通过集成面部表情识别服务将非语言情感线索隐式融入大语言模型对话，实现无需用户显式控制的流畅多模态交互。
摘要：
我们提出了共情提示，一种新颖的多模态人机交互框架，它通过隐式的非语言上下文丰富大语言模型（LLM）对话。该系统集成了商业面部表情识别服务以捕捉用户的情感线索，并将其作为上下文信号嵌入提示过程中。与传统多模态界面不同，共情提示不需要用户显式控制；相反，它通过情感信息无干扰地增强文本输入，以实现对话和流畅性对齐。该架构模块化且可扩展，允许集成额外的非语言模块。我们描述了通过本地部署的DeepSeek实例实现的系统设计，并报告了初步的服务和可用性评估（N=5）。结果表明，非语言输入能够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。除了这一概念验证外，共情提示还指向了聊天机器人中介通信中的应用，特别是在医疗或教育等领域，这些领域中用户的情感信号至关重要，但在言语交流中往往难以察觉。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
