@article{4836,
  author = {Pit Pichappan},
  title = {Multidimensional Hallucination Risk Across AI Models: Frequency, Confidence, Accuracy Mismatch, Detection, and Trust Impact},
  journal = {Journal of Intelligent Computing},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jic/2026/17/4/179-199},
  url = {https://www.dline.info/jic/fulltext/v17n4/jicv17n4_1.pdf},
  abstract = {Large language models (LLMs) frequently generate plausible but factually inaccurate outputs, commonly
known as hallucinations. Moving beyond treating hallucination as a simple binary factuality problem, this
study evaluates it as a multidimensional risk encompassing generation frequency, confidence accuracy
mismatch, detectability, and downstream trust impact. Analyzing a comprehensive dataset of 10,000
interactions across six frontier AI models and platforms, we assessed hallucination prevalence, epistemic
calibration, and human consequences. Results reveal distinct variations in hallucination rates, with Llama
and Mistral exhibiting the highest frequencies. Crucially, we identified a pronounced confidence accuracy
mismatch across systems; notably, Mistral presented the most concerning risk configuration by combining
high hallucination rates, maximum AI confidence, and the lowest detection rates. Conversely, GPT-5
demonstrated the most favorable factual accuracy profile. Cross-tool analysis demonstrated that
hallucination occurrence, rather than specific tool identity, serves as the primary driver of severe trust
erosion. Specifically, the mere presence of an error caused a massive trust penalty that subsequent detection
alone could not resolve. These findings proved that AI hallucination is a systemic, cross-platform reliability
challenge. Mitigating its impact requires integrated strategies addressing confidence calibration, proactive
detection mechanisms, and user interface designs that foster appropriate skepticism, rather than relying
solely on reducing raw factual error rates.},
}