恰到好处：理解用户对个性化 LLM 增强叙事干预的感知
Perfectly to a Tee: Understanding User Perceptions of Personalized LLM-Enhanced Narrative Interventions
AI总结：
本文利用 LLM 生成贴合个体困境的个性化叙事，并通过346名年轻成人的研究证明其在共鸣、反思和削弱消极想法方面优于人工故事。
摘要：
关于克服个人困境的故事能够有效展示心理学理论在现实生活中的应用，但它们可能无法与个体的经历产生共鸣。在本研究中，我们利用大型语言模型（LLM）生成定制化叙事，以承认并应对个体面临的独特挑战性想法和情境。我们的研究在两种场景下纳入了346名年轻成人，结果表明，个性化的 LLM 增强故事在传达关键要点、促进反思以及降低对消极想法的认同方面，被认为优于人类撰写的故事。这些故事不仅被认为更具关联性，而且在真实性上与人类撰写的故事相似，凸显了 LLM 在帮助年轻成人应对困境方面的潜力。本研究结果为未来基于叙事的数字心理健康干预提供了关键设计考量，例如需要在保持关联性的同时避免变得不合情理，并优化 AI 增强内容的措辞与语气。
英文摘要：
Stories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create tailored narratives that acknowledge and address unique challenging thoughts and situations faced by individuals. Our study, involving 346 young adults across two settings, demonstrates that personalized LLM-enhanced stories were perceived to be better than human-written ones in conveying key takeaways, promoting reflection, and reducing belief in negative thoughts. These stories were not only seen as more relatable but also similarly authentic to human-written ones, highlighting the potential of LLMs in helping young adults manage their struggles. The findings of this work provide crucial design considerations for future narrative-based digital mental health interventions, such as the need to maintain relatability without veering into implausibility and refining the wording and tone of AI-enhanced content.
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。
