The output of language models can be considered a form of hallucination because these models do not possess real understanding or factual knowledge about the underlying concepts. Instead, they generate text by statistically predicting and assembling words based on vast training data and the input prompts, without true comprehension.

Since the training data can contain inaccuracies, conflicting information, or low-frequency facts that are essentially random, models can produce plausible-sounding but false statements. Unlike humans, language models have no awareness or grounding in real-world concepts; their generation is essentially an amalgam of stored patterns and input cues rather than grounded knowledge.

Furthermore, evaluation methods that reward accuracy without penalizing guessing encourage models to produce confident but incorrect answers rather than admit uncertainty or abstain from answering. This challenge is intrinsic to how language models generate fluent language: they lack external verification or true understanding, making hallucinations an inherent characteristic of their outputs rather than a malfunction.

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| a. What's with the -minus votes?

| b. I was only quoting ChatGPT :]