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You’re restating two claims that sound intuitive but don’t actually hold up when examined: 1. “LLMs just mimic the training set, so sounding like they understa

You’re restating two claims that sound intuitive but don’t actually hold up when examined: 1. “LLMs just mimic the training set, so sounding like they understand doesn’t imply understanding.” This is the magic argument reskinned. Transformers aren’t copying strings, they’re constructing latent representations that capture relationships, abstractions, and causal structure because doing so reduces loss. We know this not by philosophy, but because mechanistic interpretability has repeatedly uncovered internal circuits representing world states, physics, game dynamics, logic operators, and agent modeling. “It’s just next-token prediction” does not prevent any of that from occurring. When an LLM performs multi-step reasoning, corrects its own mistakes, or solves novel problems not seen in training, calling the behavior “mimicry” explains nothing. It’s essentially saying “the model can do it, but not for the reasons we’d accept,” without specifying what evidence would ever convince you otherwise. Imaginary distinction. 2. “Humans predict the world, but LLMs only predict text, so humans understand but LLMs don’t.” This is a distinction without the force you think it has. Humans also learn from sensory streams over which they have no privileged insight into the generative process. Humans do not know the “real world”; they learn patterns in their sensory data. The fact that the data stream for LLMs consists of text rather than photons doesn’t negate the emergence of internal models. An internal model of how text-described worlds behave is still a model of the world. If your standard for “understanding” is “being able to successfully predict consequences within some domain,” then LLMs meet that standard, just in the domains they were trained on, and today's state of the art is trained on more than just text. You conclude that “therefore the LLM understands nothing.” But that’s an all-or-nothing claim that doesn’t follow from your premises. A lack of sensorimotor grounding limits what kinds of understanding the system can acquire; it does not eliminate all possible forms of understanding. Wouldn't the birds that have the ability to navigate from the earth's magnetic field soon say humans have no understanding of electromagnetism ? They get trained on sensorimotor data humans will never be able to train on. If you think humans have access to the "real world" then think again. They have a tiny, extremely filtered slice of it. Saying “it understands nothing because autoregression” is just another unfalsifiable claim dressed as an explanation.

HarHarVeryFunny 1y ago
> This is the magic argument reskinned. Transformers aren’t copying strings, they’re constructing latent representations that capture relationships, abstractions, and causal structure because doing so reduces loss. Sure (to the second part), but the latent representations aren't the same as a humans. The human's world that they have experience with, and therefore representations of, is the real word. The LLM's world that they have experience with, and therefore representations of, is the world of words. Of course an LLM isn't literally copying - it has learnt a sequence of layer-wise next-token predictions/generations (copying of partial embeddings to next token via induction heads etc), with each layer having learnt what patterns in the layer below it needs to attend to, to minimize prediction error at that layer. You can characterize these patterns (latent representations) in various ways, but at the end of the day they are derived from the world of words it is trained on, and are only going to be as good/abstract as next token error minimization allows. These patterns/latent representations (the "world model" of the LLM if you like) are going to be language-based (incl language-based generalizations), not the same as the unseen world model of the humans who generated that language, whose world model describes something completely different - predictions of sensory inputs and causal responses. So, yes, there is plenty of depth and nuance to the internal representations of an LLM, but no logical reason to think that the "world model" of an LLM is similar to the "world model" of a human since they live in different worlds, and any "understanding" the LLM itself can be considered as having is going to be based on it's own world model. > Saying “it understands nothing because autoregression” is just another unfalsifiable claim dressed as an explanation. I disagree. It comes down to how do you define understanding. A human understands (correctly predicts) how the real world behaves, and the effect it's own actions will have on the real world. This is what the human is predicting. What an LLM is predicting is effectively "what will I say next" after "the cat sat on the". The human might see a cat and based on circumstances and experience of cats predict that the cat will sit on the mat. This is because the human understands cats. The LLM may predict the next word as "mat", but this does not reflect any understanding of cats - it is just a statistical word prediction based on the word sequences it was trained on, notwithstanding that this prediction is based on the LLMs world-of-words-model.
- famouswaffles 1y ago

HarHarVeryFunny 1y ago

> This is the magic argument reskinned. Transformers aren’t copying strings, they’re constructing latent representations that capture relationships, abstractions, and causal structure because doing so reduces loss. Sure (to the second part), but the latent representations aren't the same as a humans. The human's world that they have experience with, and therefore representations of, is the real word. The LLM's world that they have experience with, and therefore representations of, is the world of words. Of course an LLM isn't literally copying - it has learnt a sequence of layer-wise next-token predictions/generations (copying of partial embeddings to next token via induction heads etc), with each layer having learnt what patterns in the layer below it needs to attend to, to minimize prediction error at that layer. You can characterize these patterns (latent representations) in various ways, but at the end of the day they are derived from the world of words it is trained on, and are only going to be as good/abstract as next token error minimization allows. These patterns/latent representations (the "world model" of the LLM if you like) are going to be language-based (incl language-based generalizations), not the same as the unseen world model of the humans who generated that language, whose world model describes something completely different - predictions of sensory inputs and causal responses. So, yes, there is plenty of depth and nuance to the internal representations of an LLM, but no logical reason to think that the "world model" of an LLM is similar to the "world model" of a human since they live in different worlds, and any "understanding" the LLM itself can be considered as having is going to be based on it's own world model. > Saying “it understands nothing because autoregression” is just another unfalsifiable claim dressed as an explanation. I disagree. It comes down to how do you define understanding. A human understands (correctly predicts) how the real world behaves, and the effect it's own actions will have on the real world. This is what the human is predicting. What an LLM is predicting is effectively "what will I say next" after "the cat sat on the". The human might see a cat and based on circumstances and experience of cats predict that the cat will sit on the mat. This is because the human understands cats. The LLM may predict the next word as "mat", but this does not reflect any understanding of cats - it is just a statistical word prediction based on the word sequences it was trained on, notwithstanding that this prediction is based on the LLMs world-of-words-model.

famouswaffles 1y ago