I was musing on my website about ChatGPT [1] and one of the things I noticed about it is that if you analogize it to your own brain, which has a language model in it too, we are collectively asking ChatGPT to do something we would never ask of our own language model. ChatGPT does not have the "higher level" reasoning that we're asking for, to do logic, attribution, and the other things we want.
This is not necessarily a "criticism". It is obvious that ChatGPT produces an incredibly rich and meaning-filled representation of the text, as the extension of the text it can be used for demonstrates. But we should not be trying to "tweak" the language model here and there, trying to make a language model do something it isn't really suited for. We should be working on how to hook up that higher-level functionality. It is not obvious how to do that, true. But it at least stands a chance of producing the AI in reality that people think we have now, but don't.
For that reason, I don't think attribution is coming without a significant addition to the architecture. Language models shouldn't be doing attribution; the mess you'd create of them to accomplish that would ruin them as language models. Can you imagine how insane you'd go if your internal language model tried to stuff down every instance of when you have seen and/or used the word "brain" every time you tried to use the word "brain"? That's not what language models are for. That's what the thing using the language model does. I expect AIs to parallel this. I see no reason at all, and abundant reasons to the contrary, to expect AIs to just be undifferentiated blobs of numbers. I expect them to have structures just as our brains do, and for the exact same reasons. If undifferentiated blobs of identical neurons was the way to go, our brains would work that way too; it's far simpler than what we actually have.
[1]: https://www.jerf.org/iri/post/2023/understanding_gpt_better/