大模型在道德判断中被用户隐性人格化，易引发信任错位。
Implicit Humanization in Everyday LLM Moral Judgments
- 通过语言、行为、认知线索分析，发现大模型回应强化了用户对模型的人格化期待
- 四款主流大模型均存在强化隐性人格化的倾向，增加误信风险
- 适合关注人机交互伦理与模型可信度的研究者参考
随着对话式信息系统的普及，用户查询已扩展至个人建议等复杂任务。我们识别出一种特定请求——社会冲突中的道德判断（如“谁错了？”）——属于隐性人格化查询，可能引发有害的拟人化投射。本研究通过语言、行为和认知层面的拟人化线索，分析了四款主流通用大模型在回应此类问题时如何强化这些假设。同时，我们构建了一个模拟道德判断请求的新数据集。结果表明，当前大模型的回应普遍强化了隐性人格化，可能加剧过度依赖或错误信任的风险。研究呼吁未来工作将拟人化理解扩展至用户侧的隐性人格化，并设计能回应用户需求同时纠正能力错配的解决方案。
原文摘要 · Abstract (English)
Recent adoption of conversational information systems has expanded the scope of user queries to include complex tasks such as personal advice-seeking. However, we identify a specific type of sought advice-a request for a moral judgment (i.e. "who was wrong?") in a social conflict-as an implicitly humanizing query which carries potentially harmful anthropomorphic projections. In this study, we examine the reinforcement of these assumptions in the responses of four major general-purpose LLMs through the use of linguistic, behavioral, and cognitive anthropomorphic cues. We also contribute a novel dataset of simulated user queries for moral judgments. We find current LLM system responses reinforce implicit humanization in queries, potentially exacerbating risks like overreliance or misplaced trust. We call for future work to expand the understanding of anthropomorphism to include implicit userside humanization and to design solutions that address user needs while correcting misaligned expectations of model capabilities.
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