让AI通过表情识别理解用户情绪，提升对话自然度
Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations
- 用面部识别捕捉用户情绪，自动融入对话提示中
- 5人测试显示情绪信息有效融入回复，对话更流畅
- 适合医疗教育等需关注情绪的交互场景
我们提出情感提示（Empathic Prompting）框架，通过集成商用面部表情识别服务，在多模态人机交互中引入隐含的非语言情绪线索，并将其作为上下文信号嵌入提示过程。与传统多模态界面不同，该方法无需用户主动操作，能无感增强文本输入的情感信息，实现对话连贯性与自然度的对齐。系统架构模块化且可扩展，支持接入更多非语言模态。基于本地部署的DeepSeek模型实现，初步服务与可用性评估（N=5）表明，非语言输入被一致整合到连贯的LLM输出中，参与者普遍认可对话流畅性。本工作为聊天机器人在医疗、教育等情绪敏感领域提供新思路。
原文摘要 · Abstract (English)
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.
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