I agree that it's probably unfalsifiable in the sense of proving it definitively based on something like static analysis of the model itself.
But that doesn't mean that we can't, in theory, give the LLM a battery of tests that it should perform well (though not perfectly) on if it has a world model, and poorly (though not fail totally) on if it doesn't.
It's inherently a probabilistic system, so testing it in a probabilistic manner seems perfectly apt. Again: no, this will not produce a definitive result, due to that probabilistic nature—but it can produce an indicative one, and running the same test on multiple related LLMs, or similar tests on the same LLM, should help to smooth out noise in the results.
(...of course, this only works if the tests are designed well, and I don't have enough specific understanding of LLMs to know how one would go about doing that in a rigorous manner!)
I don't think its nearly as cut-and-dry as that. Even if you tried to make tests to differentiate world-model from non-world-model, all you'd end up concluding is:
If the AI has a world model, its world-model doesn't have features that allow it to do what I tested for.
In theory, if you have some people who know what they're doing, they could design enough different kinds of world-model tests that they could significantly reduce the likelihood of the LLM having a world model.
I think I would probably word the distinction I would draw as "it is technically unfalsifiable, but it is not untestable."
Comments
I agree that it's probably unfalsifiable in the sense of proving it definitively based on something like static analysis of the model itself.
But that doesn't mean that we can't, in theory, give the LLM a battery of tests that it should perform well (though not perfectly) on if it has a world model, and poorly (though not fail totally) on if it doesn't.
It's inherently a probabilistic system, so testing it in a probabilistic manner seems perfectly apt. Again: no, this will not produce a definitive result, due to that probabilistic nature—but it can produce an indicative one, and running the same test on multiple related LLMs, or similar tests on the same LLM, should help to smooth out noise in the results.
(...of course, this only works if the tests are designed well, and I don't have enough specific understanding of LLMs to know how one would go about doing that in a rigorous manner!)
I don't think its nearly as cut-and-dry as that. Even if you tried to make tests to differentiate world-model from non-world-model, all you'd end up concluding is:
If the AI has a world model, its world-model doesn't have features that allow it to do what I tested for.
In theory, if you have some people who know what they're doing, they could design enough different kinds of world-model tests that they could significantly reduce the likelihood of the LLM having a world model.
I think I would probably word the distinction I would draw as "it is technically unfalsifiable, but it is not untestable."