I think the most descriptive title I could give an LLM is "bias". An LLM is not "biased", it is bias; or at the very least, it's a good imitation of the system of human thinking/perception that we call bias.

An LLM is a noise generator. It generates tokens without logic, arithmetic, or any "reason" whatsoever. The noise that an LLM generates is not truly random. Instead, the LLM is biased to generate familiar noise. The LLM itself is nothing more than a model of token familiarity. Nothing about that model can tell you why some tokens are more familiar with others, just like an accounting spreadsheet can't tell you why it contains a list of charges and a summation next to the word "total". It could just as easily contain the same kind of data with an entirely different purpose.

What an LLM models is written human text. Should we really expect to not be surprised by the power and versatility of human-written text?

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It's clear that these statistical models are very good at thoughtless tasks, like perception and hallucination. It's also clear that they are very bad at thoughtful tasks like logic and arithmetic - the things that traditional software is made of. What no one has really managed to figure out is how to bridge that gap.

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esafak 3 days ago | parent [-]

LLMs today are great coders. Most humans are worse.

	▲	inglor_cz 3 days ago \| parent [-]
		LLMs ingested a lot of high-quality code during their training, plus LLMs being capable of programming is a huge commercial use case, so no wonder that they are good at coding. My experience, though, is that they aren't good at defining the task to be coded, or thinking about some unexpected side-effects. Code that will be left for them to develop freely will likely become bloated quite fast.