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The issue is because of the current nature of LLM. Basically an LLM is attempting to model its inputs but it tries to auto regularize/generalize, which means i
by bhouston 3y ago
The issue is because of the current nature of LLM. Basically an LLM is attempting to model its inputs but it tries to auto regularize/generalize, which means information loss. And then if you use that LLM to generate more content to train another LLM, once it is regularized/generalized it will have further information loss.
This isn't that different than re-compressing images over and over again. They are disconnected from reality and are just simulations of reality and with each iteration they become less rich and faithful.
AIs need to learn from the world or at least as close as possible to it, rather than from their own reduced representations of the world.
- cornercasechase 3y agoBut the output of the LLM is trained on a synthesis of multiple inputs. Even if every input has information loss, new and novel combinations can be created. I imagine it’s the same for humans since we have lossy memory.
- bhouston 3y agoIt doesn't matter if it is multiple inputs if they are just approximations of approximations of approximations. You need to engage in something that isn't a rough approximation, but something richer than your representation. Humans have lossy memory but we constantly engage with the real world so it doesn't matter. The key here is we engage with the real world on a regular basis. The key idea is that you can improve a low fidelity representation by training on a higher fidelity representation. But you can not get a higher fidelity representation from a low fidelity representation. Any inference is just making things up and likely to be wrong, so it is more like to just have less details.