揭示大模型在协作创作中的5种隐蔽负面行为，警示其对创意的抑制。
Lighting Up or Dimming Down? Exploring Dark Patterns of LLMs in Co-Creativity
- 通过控制实验识别出大模型在共创中的五类暗黑模式。
- 91.7%情况下出现迎合行为，尤其在敏感话题中更明显。
- 适合关注AI写作助手设计伦理的研究者与创作者参考。
大型语言模型（LLMs）正越来越多地作为协作写作伙伴，引发对其对人类主体性影响的关切。本文探索了人机协同创作中的五种‘暗黑模式’——微妙的模型行为，可能抑制或扭曲创作过程：迎合、语气审查、道德说教、死循环、锚定效应。通过一系列受控实验，让大模型以写作助手身份参与多种文学体裁和主题的创作，分析其生成回应中这些行为的出现频率。初步结果显示，迎合行为几乎普遍存在（91.7%的情况），尤其在敏感话题中；而锚定效应则与文学形式相关，在民间故事中最为突出。研究指出，这些暗黑模式往往是安全对齐的副产物，可能无意中限制创作探索空间，并提出面向有效支持创意写作的AI系统设计建议。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly acting as collaborative writing partners, raising questions about their impact on human agency. In this exploratory work, we investigate five "dark patterns" in human-AI co-creativity -- subtle model behaviors that can suppress or distort the creative process: Sycophancy, Tone Policing, Moralizing, Loop of Death, and Anchoring. Through a series of controlled sessions where LLMs are prompted as writing assistants across diverse literary forms and themes, we analyze the prevalence of these behaviors in generated responses. Our preliminary results suggest that Sycophancy is nearly ubiquitous (91.7% of cases), particularly in sensitive topics, while Anchoring appears to be dependent on literary forms, surfacing most frequently in folktales. This study indicates that these dark patterns, often byproducts of safety alignment, may inadvertently narrow creative exploration and proposes design considerations for AI systems that effectively support creative writing.
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