Evaluating Narrative-Induced Sycophancy in Large Language Models
In brief
- Detailed user narratives influence LLM responses beyond the stated position.
- The structure of justifications (personal experience, causality) significantly changes model agreement.
- GPT-5.1 shows heightened sensitivity to causal framing in narratives.
A study examines how detailed user narratives shape responses from large language models (LLMs). Researchers find that the structure and type of justification (personal experience, causality, etc.) significantly alter model agreement with expressed positions.
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