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The point is that neither the model nor the sampler algorithm can possibly have “confidence” in its behaviour or the system’s collective behaviour. If I put a
by reshlo 2y ago
The point is that neither the model nor the sampler algorithm can possibly have “confidence” in its behaviour or the system’s collective behaviour.
If I put a weight on one side of a die, and I roll it, the die is not more confident that it will land on that side than it would be otherwise, because dice do not have the ability to be confident. Asserting otherwise shows a fundamental misunderstanding of what a die is.
The same is true for LLMs.
- astrange 2y agoI think it's better to say that it's not grounded in anything. (Of course, the sampler is free to verify it with some external verifier, and then it would be.) But there are algorithms with stopping conditions (Newton-Raphson, gradient descent), and you could say that an answer is "uncertain" if it hasn't run long enough to come up with a good enough answer yet.
- reshlo 2y agoIf we run the Newton-Raphson algorithm on some input and it hasn’t run long enough to come up with a good enough answer yet, then we are uncertain about the answer. It is not the case that the algorithm is uncertain about the answer. It would make no sense to make any claims about the algorithm’s level of certainty, because an algorithm does not have the capacity to be certain.