Article

LLMs Don’t hallucinate — we just misunderstand them

The very impressive progress of large language models (LLMs) in the past few years has been marred by what's commonly called "hallucinations": very confident but patently wrong answers to prompts, such as telling users to eat a rock a day for health benefits, or adding glue as a pizza topping. Implied by the use of the word "hallucination" to describe such things is the idea that they are unwanted deviations from a norm, a hiccup or malfunction of the system. But when you dig into the technical and conceptual underpinnings of how these models work, you'll see that the problem is not in how they function —they're working perfectly fine— but in the expectations we have of them.

More and more, we have seen LLMs being used for areas such as marketing, journalism, and even finance. That works, to an extent, because they've been trained on unimaginably large amounts of text — so large that that models have been able to form statistical patterns relating to certain domains of knowledge. These patterns are then 'baked into' what we call a large language models. Put very simply, when you prompt an LLM it generates a bunch of words based on the probability of how likely those words or sentences are to occur after the preceding words and sentences and paragraphs, whatever they may be. It's a very sophisticated bit of statistical prediction. There is no true comprehension, understanding of context or emotions, or thinking involved. A hallucination, then, isn't a blip or an example of a crossed wire, but a natural outcome of the way large language models work.

At Mesoica we believe there's a clear value in the application of LLMs, but only in the context of secure guardrails and a good understanding not only of their abilities but also of their limitations and risks.

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