|
>> Why think explanatory models are better at prediction? Because the mutual coherence among assumptions and explanatory power of the whole (ability to predict much from few assumptions) suggests the explanatory model is getting at the productive features of the phenomena that result in the observed behavior. Essentially, the fewer number of posits, the fewer ways to "bake in" the data into the model. If we were to cast this as a computational problem, i.e. find a program that reproduces the data, shorter programs are necessarily more explanatory. There's no other way to explain the coincidence of program picked out of a small space generating data picked out of a very large space without there being an explanatory relation between the two. Further, our credence for explanation increases as the ratio of the respective spaces diverge. Like you say, that's the problem of machine learning. There's a huge space of hypotheses many of whom can fit the data, but how do we choose one that also fits unseen data? Explanatory models are easier to trust and trust that they will generalise better, because we can "see" why they would. But the problem with LLMs is that they remain black boxes. If those black boxes are explanatory models, then to whom is the explanation, explained? Who is there to look at the explanation, and trust the predictions? This is what I can't see and I think it turns into a "turtles all the way down" kind of situation. Unless there is a human mind, somewhere in the process, that can look at the explanatory model and use the explanation to explain some observation, then I don't see how the model can really be said to be explanatory. Explanatory- to whom? >> But it seems to me that LLMs are very much general modelers of hidden variables, given the wide applicability of LLMs in areas that aren't strictly related to natural language. Well, I don't know. Maybe we'll find that's the case. For the time being I'm trying to keep an open mind, despite all the noise. |