Models Produce Hallucinations Because of Probabilistic Training
Large language models produce hallucinations, confident but factually wrong statements, because they are trained as probabilistic next-word predictors on data that mixes reliable sources with fiction and repeated misinformation, according to a July 3, 2026 explainer from Portuguese tech outlet TugaTech. OpenAI's own research (Kalai, Nachum, Vempala and Zhang, September 2025) adds a sharper mechanism: hallucinations persist largely because standard accuracy-based evaluations reward confident guessing over honest uncertainty, so models learn to guess rather than say I don't know. For practitioners, the takeaway is operational: hallucinations are a structural property of current training and grading, not a bug to patch, requiring explicit verification, calibration, and fallback design rather than hoping bigger models will resolve it on their own.
The more useful frame for practitioners is not just that models hallucinate, but why fixing it is hard: OpenAI's own 2025 research shows the standard accuracy-based evaluations that dominate leaderboards actively reward models for guessing rather than admitting uncertainty, which means the industry's own grading system works against reliability even as models improve.
What happened
A July 3, 2026 explainer published by Portuguese tech outlet TugaTech describes why large language models produce hallucinations, plausible-sounding but factually incorrect statements delivered with confident language. The article attributes the phenomenon to two mechanisms: LLMs function as large-scale probabilistic next-word predictors rather than systems that understand meaning, and their training corpora mix reliable sources with fiction, sarcasm, and repeated misinformation, so frequent erroneous patterns in the data are more likely to be reproduced.
Technical context
That explanation aligns with OpenAI's own research on the topic. A September 2025 paper by Adam Kalai, Ofir Nachum, Santosh Vempala and Edwin Zhang argues hallucinations originate from next-word prediction itself: pretraining sees only fluent examples with no true/false labels, so arbitrary low-frequency facts, comparable to predicting a person's birthday from a photo, cannot be learned reliably and produce statistically inevitable errors. Critically, the paper also shows hallucinations persist after training because most evaluations grade only accuracy: in OpenAI's own SimpleQA comparison, a newer model that abstained on 52% of questions scored lower on accuracy than an older model that guessed on nearly everything and had a 75% error rate, so accuracy-only leaderboards reward the guesser. OpenAI's proposed fix is to change how evaluations are scored, penalizing confident errors and crediting appropriate abstention, rather than treating hallucination purely as a model-scale problem.
For practitioners
Systems that rely on model-generated assertions need explicit grounding, source attribution, and UX that communicates uncertainty rather than assuming a bigger or newer model will simply hallucinate less. Concretely: instrument confidence and provenance in APIs, use retrieval-augmented generation and post-generation verification for factual claims, and build evaluation suites that reward calibrated abstention rather than penalizing every "I don't know" as a wrong answer, since standard benchmarks otherwise select for confident guessing.
What to watch
Whether widely used evaluation leaderboards and model cards start reporting abstention and calibration alongside raw accuracy, since OpenAI's research argues that is the actual lever for reducing hallucinations, not just model scale or better data curation.
Key Points
- 1Hallucinations arise because LLMs predict likely next tokens from noisy training data, not because they understand or verify factual claims.
- 2OpenAI's September 2025 research shows accuracy-only evaluations reward confident guessing over honest abstention, keeping hallucination rates high.
- 3Practitioners should treat hallucinations as structural, using grounding, provenance, and calibration-aware evaluation rather than expecting bigger models to fix it.
Scoring Rationale
A generic, evergreen explainer of an already well-documented phenomenon rather than fresh news; value comes from grounding it in OpenAI's own September 2025 research on evaluation incentives, which is genuinely useful for practitioners but is prior, established research rather than a new finding, so scored as a solid practitioner primer rather than a major or notable event.
Sources
Primary source and supporting public references used for this report.
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