大语言模型谬误：人工智能辅助认知工作流中的误归因
The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
AI总结：
本文探讨大语言模型使用如何导致用户错误归因自身能力，提出LLM谬误概念，分析其在认知、语言、分析和创作领域的影响，并提出教育、招聘和AI素养的启示。
摘要：
大语言模型（LLM）快速融入日常工作流，改变了个体在写作、编程、分析和多语言交流等认知任务中的表现。尽管先前研究关注模型可靠性、幻觉和用户信任校准，但较少关注LLM使用如何重塑用户对自己能力的感知。本文引入LLM谬误，即个体将LLM辅助输出误认为自身独立能力的归因错误，导致感知能力和实际能力之间系统性偏离。我们论证LLM的不透明性、流畅性和低摩擦交互模式模糊了人类与机器贡献的界限，使用户根据输出而非生成过程推断能力。本文将LLM谬误置于自动化偏差、认知卸载和人机协作的现有文献中，并区分其作为AI中介工作流特有的归因扭曲形式。我们提出其潜在机制的概念框架和跨计算、语言、分析和创作领域的表现类型学。最后，我们探讨其对教育、招聘和AI素养的影响，并提出实证验证的方向。我们还提供透明的人机协作方法论。本文为理解生成式AI系统如何不仅增强认知表现，还重塑自我认知和感知专业性奠定了基础。
英文摘要：
The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and multilingual communication. While prior research has focused on model reliability, hallucination, and user trust calibration, less attention has been given to how LLM usage reshapes users' perceptions of their own capabilities. This paper introduces the LLM fallacy, a cognitive attribution error in which individuals misinterpret LLM-assisted outputs as evidence of their own independent competence, producing a systematic divergence between perceived and actual capability. We argue that the opacity, fluency, and low-friction interaction patterns of LLMs obscure the boundary between human and machine contribution, leading users to infer competence from outputs rather than from the processes that generate them. We situate the LLM fallacy within existing literature on automation bias, cognitive offloading, and human-AI collaboration, while distinguishing it as a form of attributional distortion specific to AI-mediated workflows. We propose a conceptual framework of its underlying mechanisms and a typology of manifestations across computational, linguistic, analytical, and creative domains. Finally, we examine implications for education, hiring, and AI literacy, and outline directions for empirical validation. We also provide a transparent account of human-AI collaborative methodology. This work establishes a foundation for understanding how generative AI systems not only augment cognitive performance but also reshape self-perception and perceived expertise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
