"What if she doesn't feel the same?" What Happens When We Ask AI for Relationship Advice

TL;DR

This study explores how people perceive and evaluate AI-generated romantic relationship advice, revealing high satisfaction and improved attitudes after exposure, which may influence trust in AI support systems.

Contribution

It provides empirical insights into user perceptions of LLMs in personal advice contexts and shows how positive experiences can enhance trust in AI systems.

Findings

Participants reported high satisfaction with AI advice.

Perceived reliability and helpfulness strongly correlated with satisfaction.

Attitudes toward LLMs improved after receiving advice.

Abstract

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.