Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild

The paper introduces JudgeGPT and RogueGPT, a dual‑axis framework that separates authenticity from attribution to study how people detect hallucinations and disinformation from foundation models. Using 918 evaluations on five models, including GPT‑4 and Llama‑2, the authors apply Structural Causal Models to test causal hypotheses about detection accuracy. Results show political orientation has little effect, while familiarity with fake news mediates detection, and a "fluency trap" in GPT‑4 outputs makes them hard to distinguish from human text, implying pre‑bunking should focus on cognitive source monitoring.

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• llms
• computer-vision
• safety