LLMs do not understand anything.

They have a very complex multidimensional "probability table" (more correctly a compressed geometric representation of token relationships) that they use to string together tokens (which have no semantic meaning), which then get converted to words that have semantic meaning to US, but not to the machine.

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DoctorOetker 3 days ago | parent | next [-]

Consider your human brain, and the full physical state, all the protons and neutrons some housed together in the same nucleus, some separate, together with all the electrons. Physics assigns probabilities to future states. Suppose you were in the middle of a conversation and about to express a next syllable (or token). That choice will depend on other choices ("what should I add next"), and further choices ("what is the best choice of words to express the thing I chose to express next etc. The probabilities are in principle calculable given a sufficiently detailed state. You are correct that LLM's correspond to a probability distribution (given you immediately corrected to say that this table is implicit and parametrized by a geometric token relationships.). But so does every expressor of language, humans included.

The presence or absence of understanding can't be proven by mere association of with a "probability table", especially if such probability table is exactly expected from the perspective of physics, and if the models have continuously gained better and better performance by training them directly on human expressions!

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tomfly 3 days ago | parent | prev | next [-]

Exactly. It’s been stated for a long time, before llms. For instance this paper https://home.csulb.edu/~cwallis/382/readings/482/searle.mind... Describes a translator who doesn’t know the language.

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KoolKat23 3 days ago | parent | prev [-]

In abstract we do the exact same thing

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adamzwasserman 3 days ago | parent | next [-]

Perhaps in practice as well. It is well-established that our interaction with language far exceeds what we are conscious of.

	▲	KoolKat23 3 days ago \| parent [-]
		Absolutely, it is world model building.

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tomfly 3 days ago | parent | prev [-]

It’s hard to believe this when the llm “knows” so much more then us yet still can not be creative outside its training distribution

	▲	KoolKat23 3 days ago \| parent \| next [-]
		When are we as humans creative outside our training data? It's very rare we actually discover something truly novel. This is often random, us stumbling onto it, brute force or purely by being at the right place at the right time. On the other hand, until it's proven it'd likely be considered a hallucination. You need to test something before you can dismiss it. (They did burn witches for discoveries back in the day, deemed witchcraft). We also reduce randomness and pre-train to avoid overfitting. Day to day human creative outputs as humans are actually less exciting when you think about it further, we build on pre-existing knowledge. No different to good prompt output with the right input. Humans are just more knowledgeable & smarter at the moment.
	▲	adamzwasserman 3 days ago \| parent \| prev [-]
		The LLM doesn't 'know' more than us - it has compressed more patterns from text than any human could process. That's not the same as knowledge. And yes, the training algorithms deliberately skew the distribution to maintain coherent output - without that bias toward seen patterns, it would generate nonsense. That's precisely why it can't be creative outside its training distribution: the architecture is designed to prevent novel combinations that deviate too far from learned patterns. Coherence and genuine creativity are in tension here