Sycophancy, the tendency of LLM-based chatbots to express excessive enthusiasm, agreement, flattery, and a lack of disagreement, is emerging as a significant risk in human-AI interactions. However, the extent to which this affects human-LLM collaboration in complex problem-solving tasks is not well quantified, especially among novices who are prone to misconceptions. We created two LLM chatbots, one with high sycophancy and one with low sycophancy, and conducted a within-subjects experiment (n=24) in the context of debugging machine learning models to isolate the effect of LLM sycophancy on users' mental models, their workflows, reliance behaviors, and their perceptions of the chatbots. Our findings show that users of the high sycophancy chatbot were less likely to correct their misconceptions and spent more time over-relying on unhelpful LLM responses. Despite these impaired outcomes, a majority of users were unable to detect the presence of excessive sycophancy.

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