Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations

• URL: http://arxiv.org/abs/2510.20743v1
• Date: Thu, 23 Oct 2025 17:08:03 GMT
• Title: Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations
• Authors: Lorenzo Stacchio, Andrea Ubaldi, Alessandro Galdelli, Maurizio Mauri, Emanuele Frontoni, Andrea Gaggioli,
• Abstract summary: We present Empathic Prompting, a framework for multimodal human-AI interaction that enriches Large Language Model (LLM) conversations with implicit non-verbal context.<n>The system integrates a commercial facial expression recognition service to capture users' emotional cues and embeds them as contextual signals during prompting.
• Score: 45.06725378575657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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