When AI Flatters Too Much

Abstract

With the current rise of Large Language Models (LLMs), it also raises concerns that sycophantic responses may influence how users form opinions and trust in such models. This paper investigates how LLM sycophancy affects trust, perceived trustworthiness, and opinion formation among simulated users representing young adults. A 2 x 2 mixed experimental design was conducted in which simulated users between the ages of 18 and 25 interacted with either a neutral or sycophantic model across two topics: autonomous vehicles and AI in society. Users completed pre- and post-interaction questions for each topic. In addition, their open-ended reflection responses were qualitatively analyzed. Both neutral and sycophantic conditions were configured on the model Llama 3.1 8B. The results suggest that the sycophantic model increases perceived trustworthiness, while the effects on trust and opinion formation were insignificant. These results indicate that sycophantic behavior may make models appear more trustworthy even when it does not strongly influence users' opinions or trust. Results from manipulation checks show that there was only a significant difference in perceived validation between both conditions, suggesting that the perceived trustworthiness may have been influenced more by validation than by broader sycophantic behavior. Since the study uses simulated users, the results should be interpreted as exploratory rather than direct evidence of human behavior. The paper contributes an experimental setup for studying LLM sycophancy on simulated users and highlights the need for further validation with real human participants.