LLM Fallacy: Cognitive Misattribution in AI-Assisted Workflows

A recent study published on arXiv presents the concept of the 'LLM fallacy,' a cognitive error where individuals utilizing large language models (LLMs) mistakenly view AI-generated results as proof of their own skills. The researchers contend that the smooth interactions, fluency, and lack of friction in LLMs blur the lines between human input and machine output, resulting in a consistent gap between perceived abilities and actual skills. This investigation highlights how LLM engagement alters users' self-assessment in areas such as writing, programming, analysis, and multilingual communication, which has been less examined compared to issues like model reliability, hallucination, and trust calibration. The paper can be found at arXiv:2604.14807v2.