I was thinking about this "AI" stuff . . .

[highplainsdem]

45. Just one study:

ChatGPT’s Hallucination Problem: Study Finds More Than Half Of AI’s References Are Fabricated Or Contain Errors In Model GPT-4o
https://studyfinds.org/chatgpts-hallucination-problem-fabricated-references/

A Deakin University study of mental health literature reviews found that ChatGPT (GPT-4o) fabricated roughly one in five academic citations, with more than half of all citations (56%) being either fake or containing errors.

The AI’s accuracy varied dramatically by topic: depression citations were 94% real, while binge eating disorder and body dysmorphic disorder saw fabrication rates near 30%, suggesting less-studied subjects face higher risks.

Among fabricated citations that included DOIs, 64% linked to real but completely unrelated papers, making the errors harder to spot without careful verification.

I read a social media post the other day about another study showing a very high hallucination rate for AI summaries. Didn't bookmark it, so I don't have the link.

Hallucinations are inevitable with genAI.

Another study:

Language models cannot reliably distinguish belief from knowledge and fact
https://www.nature.com/articles/s42256-025-01113-8

As language models (LMs) increasingly infiltrate into high-stakes domains such as law, medicine, journalism and science, their ability to distinguish belief from knowledge, and fact from fiction, becomes imperative. Failure to make such distinctions can mislead diagnoses, distort judicial judgments and amplify misinformation. Here we evaluate 24 cutting-edge LMs using a new KaBLE benchmark of 13,000 questions across 13 epistemic tasks. Our findings reveal crucial limitations. In particular, all models tested systematically fail to acknowledge first-person false beliefs, with GPT-4o dropping from 98.2% to 64.4% accuracy and DeepSeek R1 plummeting from over 90% to 14.4%. Further, models process third-person false beliefs with substantially higher accuracy (95% for newer models; 79% for older ones) than first-person false beliefs (62.6% for newer; 52.5% for older), revealing a troubling attribution bias. We also find that, while recent models show competence in recursive knowledge tasks, they still rely on inconsistent reasoning strategies, suggesting superficial pattern matching rather than robust epistemic understanding. Most models lack a robust understanding of the factive nature of knowledge, that knowledge inherently requires truth. These limitations necessitate urgent improvements before deploying LMs in high-stakes domains where epistemic distinctions are crucial.