Large Language Models (LLMs) are increasingly being used to provide support and advice in personal domains such as romantic relationships, yet little is known about user perceptions of this type of advice. This study investigated how people evaluate advice on LLM-generated romantic relationships. Participants rated advice satisfaction, model reliability, and helpfulness, and completed pre- and post-measures of their general attitudes toward LLMs. Overall, the results showed participants' high satisfaction with LLM-generated advice. Greater satisfaction was, in turn, strongly and positively associated with their perceptions of the models' reliability and helpfulness. Importantly, participants' attitudes toward LLMs improved significantly after exposure to the advice, suggesting that supportive and contextually relevant advice can enhance users' trust and openness toward these AI systems.

翻译：大型语言模型（LLM）正越来越多地被用于提供恋爱关系等个人领域的支持与建议，但用户对此类建议的感知机制尚不明确。本研究探讨了人们对LLM生成的恋爱建议的评价方式。参与者从建议满意度、模型可靠性和帮助性三个维度进行评分，并在实验前后分别完成了对LLM总体态度的测量。总体结果显示，参与者对LLM生成的建议表现出较高的满意度。更高的满意度与参与者对模型可靠性和帮助性的感知呈显著正相关。值得注意的是，参与者在接触建议后对LLM的态度显著改善，这表明具有支持性且贴合情境的建议能够增强用户对这类人工智能系统的信任与接纳度。