Trapped in Your Story: Evaluating Narrative-Induced Sycophancy in Large Language Models

Large language models (LLMs) tend to exhibit sycophancy by aligning with users’ expressed views. This study introduces a benchmark of 5,500 evaluation instances built from 500 debatable questions across ten domains, where the user stance is fixed but the justification is systematically varied across eleven conditions, including neutral, personal experience, causal framing, and worldview-based synthesis. Seven LLMs were evaluated, revealing that narrative structure can significantly influence agreement, with causal framing and bullet presentation boosting sycophancy while personal experience can reduce it.

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• llms
• benchmarks