对话界面中通过多模态实现共情
Empathy Through Multimodality in Conversational Interfaces
AI总结：
本文基于 openCHA 构建多模态对话式健康智能体，通过情绪线索生成共情回应，并在多种情绪语调下验证其规划稳定性与人类评价一致性。
摘要：
智能体是大型语言模型（LLM）和生成式人工智能最具新兴性的应用之一，其有效性取决于应对复杂用户环境的多模态能力。对话式健康智能体（Conversational Health Agents，CHAs）是这方面的典型例证，它正在通过提供超越文本分析、并融入情绪智能的细致支持来重新定义医疗保健。本文介绍了一种基于 LLM 的 CHA，专为丰富的多模态对话而设计，尤其是在心理健康支持领域。它通过分析多模态线索，娴熟地解读并回应用户的情绪状态，从而给出具有情境意识且能引发共情共鸣的言语回应。我们的实现利用了功能多样的 openCHA 框架，并且综合评估包含以不同情绪语调表达的中性提示：悲伤、愤怒和喜悦。我们评估了所提出 CHA 规划能力的一致性和可重复性。此外，人类评估者对 CHA 的共情表达进行评判，研究结果揭示 CHA 的输出与评估者的评价之间存在显著一致性。这些结果证实了语音（不久将是多模态）情绪识别在强化 CHA 所建立的共情联结方面不可或缺的作用，巩固了其在交互式、富有同情心的数字健康解决方案前沿的地位。
英文摘要：
Agents represent one of the most emerging applications of Large Language Models (LLMs) and Generative AI, with their effectiveness hinging on multimodal capabilities to navigate complex user environments. Conversational Health Agents (CHAs), a prime example of this, are redefining healthcare by offering nuanced support that transcends textual analysis to incorporate emotional intelligence. This paper introduces an LLM-based CHA engineered for rich, multimodal dialogue-especially in the realm of mental health support. It adeptly interprets and responds to users' emotional states by analyzing multimodal cues, thus delivering contextually aware and empathetically resonant verbal responses. Our implementation leverages the versatile openCHA framework, and our comprehensive evaluation involves neutral prompts expressed in diverse emotional tones: sadness, anger, and joy. We evaluate the consistency and repeatability of the planning capability of the proposed CHA. Furthermore, human evaluators critique the CHA's empathic delivery, with findings revealing a striking concordance between the CHA's outputs and evaluators' assessments. These results affirm the indispensable role of vocal (soon multimodal) emotion recognition in strengthening the empathetic connection built by CHAs, cementing their place at the forefront of interactive, compassionate digital health solutions.
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。
