Hallucination Causes › Summary

Hallucination is a structural consequence of how language models are trained and how they generate text, not a random failure mode. The four root causes are:

• Training data issues: Web corpora contain factual errors, misinformation, and knowledge imbalances that the next-token prediction objective cannot distinguish from accurate content. The model learns errors with the same confidence as truths, weighted by their frequency in the corpus.
• Exposure bias: Teacher forcing creates a training-inference mismatch where the model is never trained to handle its own errors. Early mistakes in generation cascade across subsequent tokens because the model was only ever trained on perfect-context conditioning, not on error-containing contexts.
• Knowledge gaps: Training cutoffs, tail entity under-representation, restricted access to specialized domains, and the absence of a symbolic world model mean that many factual questions fall outside the model's reliable knowledge boundary, yet the model cannot reliably identify when it is operating outside that boundary.
• Generation pressure: Completion pressure from the always-generate objective, overconfident priors learned from confident web content, prompt-answer alignment bias, and decoding artifacts cause the model to generate confident assertions regardless of its actual knowledge state.

These causes interact and amplify each other. Addressing any one in isolation produces partial improvements. The most reliable approaches, which we will examine in the next chapter on hallucination mitigation, target multiple causes simultaneously: retrieval augmentation for knowledge gaps, better data curation for training data issues, scheduled sampling variants for exposure bias, and calibration training for generation pressure. Understanding the causes is the prerequisite for understanding which combination of mitigations is warranted for any specific deployment context.