Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918528511115264 |
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| author | Stacchio, Lorenzo Ubaldi, Andrea Galdelli, Alessandro Mauri, Maurizio Frontoni, Emanuele Gaggioli, Andrea |
| author_facet | Stacchio, Lorenzo Ubaldi, Andrea Galdelli, Alessandro Mauri, Maurizio Frontoni, Emanuele Gaggioli, Andrea |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_20743 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations Stacchio, Lorenzo Ubaldi, Andrea Galdelli, Alessandro Mauri, Maurizio Frontoni, Emanuele Gaggioli, Andrea Human-Computer Interaction Artificial Intelligence Computation and Language 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. |
| title | Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2510.20743 |