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Why Large Language Models Hallucinate, And How We’re Learning to Control It
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Language models have gone from research toys to everyday assistants in just a few years. GPT can summarise a legal contract, Claude can draft policy notes, Gemini can analyse a chart, Mistral can run efficiently on a laptop, and LLaMA is fine-tuned by hobbyists and enterprises alike.
Yet across all these systems, one flaw stubbornly remains: hallucination. It’s the tendency for a model to produce outputs that are fluent and confident but false. Think of a student answering a question in an exam by making up plausible details because they don’t want to leave the page blank.
Hallucination is more than an occasional nuisance. In safety-critical fields like healthcare, finance, and law, hallucinations can turn a helpful assistant into a liability. On the other hand, in creative writing or brainstorming, controlled hallucination is exactly what makes these systems exciting.
This dual nature makes hallucination one of the most fascinating challenges in modern AI. Let’s break it down: why it happens, how different labs are addressing it, and what the path forward might look like.
