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By definition, a model can't "know" things that are not somewhere in its training set, unless it can use a tool to query external knowledge. The problem is tha
by LeoPanthera 1y ago
By definition, a model can't "know" things that are not somewhere in its training set, unless it can use a tool to query external knowledge.
The problem is that the size of the training set required for a good model is so large, that's really hard to make a good model without including almost all known written text available.
- janalsncm 1y ago> all known written text available If phi5 is trained on synthetic data only then info on how to make drugs must be in the synthetic dataset.
- eru 1y ago> By definition, a model can't "know" things that are not somewhere in its training set, unless it can use a tool to query external knowledge. Well, it could also make inferences. Like, it could find a new mathematical proof, even if that's never in the training set.
- xwolfi 1y agoBut how, it's not like it's thinking, it's just spitting the next likely token
- createaccount99 1y agoThat is an oversimplification, and not the whole truth. Read anthropic's blog posts if you want to learn more, or ask gpt5.
- frotaur 1y agoThis can generate new text. If the abilities generalise somewhat (and there is lots of evidence they DO generalise on some level), then there is no obstacle to generating new proofs, although the farther away they are from the training data, the less likely it becomes. For an obvious example of generalisation: the models are able to write more code than there is in the dataset. If you ask it to write some specific, though easy, function, it is very unlikely it is present verbatim in the dataset, and yet the model can adapt.