AI translated articles swapped sources or added unsourced sentences with no explanation, while others added paragraphs sourced from completely unrelated material.
What are AI hallucinations? Why AIs sometimes make things up
When AI systems try to bridge gaps in their training data, the results can be wildly off the mark: fabrications and non sequiturs researchers call hallucinations.
What are AI hallucinations? Why AIs sometimes make things up
When AI systems try to bridge gaps in their training data, the results can be wildly off the mark: fabrications and non sequiturs researchers call hallucinations.
“Hallucinating” AI models help coin Cambridge Dictionary’s word of the year
Cambridge: "When an artificial intelligence hallucinates, it produces false information."
Okay so, we just found that over 50 papers published at @Neurips 2025 have AI hallucinations by @alexcdot(Alex Cui) | Twitter Thread Reader
Okay so, we just found that over 50 papers published at @Neurips 2025 have AI hallucinations I don't think people realize how bad the slop is right now It's not just that researchers from @GoogleDeepMind, @Meta, @MIT, @Cambridge_Uni are using AI - they allowed LLMs to generate hallucinations in their papers and didn't notice at all. It's insane that these made it through peer review👇
An encyclopedia formed from AI hallucinations – what could go wrong?
Feedback discovers Halupedia, an online encyclopedia that is 100 per cent generated by AI, offering such delights as the 19nd century and The Society for the Prevention of Unnecessary Tuesdays
Five times AI hallucinations embarrassed governments
From the Trump administration’s “formatting errors” to South Africa’s historic policy withdrawal, AI confabulation is infiltrating official documents.
Ars Technica Pulls Article With AI Fabricated Quotes About AI Generated Article
A story about an AI generated article contained fabricated, AI generated quotes.
OpenAI admits AI hallucinations are mathematically inevitable, not just engineering flaws
In a landmark study, OpenAI researchers reveal that large language models will always produce plausible but false outputs, even with perfect data, due to fundamental statistical and computational limits.
Halupedia: An AI-Generated Wikipedia-Style Encyclopedia of Fabricated Knowledge and Absurd AI Fabulation - BizTech Weekly
Analysis of Halupedia’s AI-driven on-demand encyclopedia model reveals real-time, non-persistent article generation that simulates authoritative references through fabricated citations and internal “canon” consistency, highlighting challenges in provenance, hallucination, moderation, and the evolving trade-offs between novelty-driven engagement and information integrity in generative AI systems.
Wikipedia:Signs of AI writing
This is a list of writing and formatting conventions typical of AI chatbots such as ChatGPT, with real examples taken from Wikipedia articles, drafts, comments, and other content. It is a field guide to help detect undisclosed AI-generated content on Wikipedia: while some of the signs may be broadly applicable, some may not apply in a non-Wikipedia context.[a] Not all text featuring these indicators is AI-generated, as the large language models that power AI chatbots are trained on human writing, including Wikipedia. Many elements of AI writing can be found in editorials, blogs, or fan fiction.
Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias
Score-based diffusion models have achieved incredible performance in generating realistic images, audio, and video data. While these models produce high-quality samples with impressive details, they often introduce unrealistic artifacts, such as distorted fingers or hallucinated texts with no meaning. This paper focuses on textual hallucinations, where diffusion models correctly generate individual symbols but assemble them in a nonsensical manner. Through experimental probing, we consistently observe that such phenomenon is attributed it to the network's local generation bias. Denoising networks tend to produce outputs that rely heavily on highly correlated local regions, particularly when different dimensions of the data distribution are nearly pairwise independent. This behavior leads to a generation process that decomposes the global distribution into separate, independent distributions for each symbol, ultimately failing to capture the global structure, including underlying grammar. Intriguingly, this bias persists across various denoising network architectures including MLP and transformers which have the structure to model global dependency. These findings also provide insights into understanding other types of hallucinations, extending beyond text, as a result of implicit biases in the denoising models. Additionally, we theoretically analyze the training dynamics for a specific case involving a two-layer MLP learning parity points on a hypercube, offering an explanation of its underlying mechanism.
The Impact of AI-Generated Text on the Internet
The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative...
The Impact of AI-Generated Text on the Internet
The proliferation of AI-generated and AI-assisted text on the internet is feared to contribute to a degradation in semantic and stylistic diversity, factual accuracy, and other negative...
Wikipedia Pauses AI-Generated Summaries After Editor Backlash
“This would do immediate and irreversible harm to our readers and to our reputation as a decently trustworthy and serious source,” one Wikipedia editor said.
The Editors Protecting Wikipedia from AI Hoaxes
WikiProject AI Cleanup is protecting Wikipedia from the same kind of misleading AI-generated information that has plagued the rest of the internet.
AI hallucinations are getting worse – and they're here to stay
An AI leaderboard suggests the newest reasoning models used in chatbots are producing less accurate results because of higher hallucination rates. Experts say the problem is bigger than that
