Why Does AI Produce Plausible Falsehoods?

A machine that continues text

A language model does not begin as a direct judge of truth. It learns distributions over expressions that follow a context, so fluency and factual accuracy can diverge.

Even when evidence is missing, pressure to complete a pattern can assemble convincing names, dates, and citations.

• Huang et al., A Survey on Hallucination in LLMs (2024)

  A scholarly survey of types, causes, evaluation, and mitigation of factual errors in large language models.

An outcome resembling memory

Human memory is reconstructive rather than a literal recording. Clues, knowledge, and expectation rebuild a past whose gaps can shift.

Similar outcomes do not prove identical causes. Human errors occur in embodied lives; model errors arise from training data, generation objectives, and prompts.

A defect that acts as a mirror

Hallucination does not show that a machine imagines as humans do. It reflects our habit of treating fluency as evidence of truth.

Retrieval, citations, calibrated uncertainty, and verification all matter. The reader must still separate plausibility from support.