It's actually no different for dice than for LLMs. Explaining accurately the reason for the exact outcome of any given dice roll someone makes would be stupendously hard. It would require lots of instrumentation and math and be poorly transferrable to another surface, another player, etc.

But even so people don't say that we don't understand how dice work.

Saying that we don't understand how LLMs work is exactly like saying we don't understand how dice, or tires, or golf ball shots work. Or like the old myth that we don't understand how bumblebees fly.

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jacquesm 4 hours ago | parent [-]

That's precisely the point: you may be able to understand dice statistically and over the course of long rolls of dice you can extract some properties of the dice. But you won't ever understand any particular roll of the dice.

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fc417fc802 2 hours ago | parent | next [-]

But importantly for dice we do understand the overarching principles that give rise to this. And dice don't output coherent sentences. Meanwhile in LLM land the analogous "roll of the dice" can result in a coherent response in natural language.

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skydhash an hour ago | parent [-]

If you use a loaded dice, you can be pretty confident about where it will lands. It may not be 100% accurate, but can be quite close to certain. Without training the weight are pure noises. After training, it leans towards coherent sentences and particular statements.

	▲	fc417fc802 25 minutes ago \| parent [-]
		Yes, and I believe my point still stands. We thoroughly understand the principle by which a loaded die can be intentionally biased despite not being able to predict the outcome of any given throw due to the system in question being a chaotic one. In contrast, we do not understand LLMs in the same way (nor biological brains). Claiming that anything of that nature is simply biased towards coherent output seems entirely reductive to me - the question is how such coherence arises in the first place. There is no meaning encoded or computation performed by the particular pathway a die travels through the chaotic landscape. Sure an argument can be made that it's "just" a next token predictor thus how is it really any different from a markov model? Yet the output is not even remotely the same.

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2 hours ago | parent | prev [-]

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