research_blog.post
Large language models don't know when they don't know. They fill gaps with plausible-sounding fiction, deliver it with unwavering confidence, and have no internal mechanism to distinguish truth from fabrication. This isn't a bug—it's a fundamental architectural feature. Here's why it matters.
A hallucination event occurs when a model generates information that is factually incorrect, nonsensical, or untethered from reality, yet presents it as factual. This is a direct result of their training objective: to predict the next most probable word in a sequence. If the training data is sparse or contradictory on a given topic, the model will "fill in the blanks" with whatever sequence of words is most statistically likely, regardless of its truth value. The model's confidence is a measure of this statistical likelihood, not of its certainty in the factual accuracy of the statement.
This post explores the anatomy of a hallucination, from token-level probability collapses to the propagation of misinformation as users uncritically accept the AI's confident assertions. We argue that user trust is the most dangerous vulnerability in the current LLM paradigm and that developing a healthy skepticism towards AI-generated content is the most critical safety skill for the modern era.