> you need a clear value chain from licensed training data to each output, through the magical linear algebra data blender that is gradient descent. Nobody know

> you need a clear value chain from licensed training data to each output, through the magical linear algebra data blender that is gradient descent. Nobody knows if your training example helped The magical linear algebra data blender that is gradient descent boils down to small additive modifications to the model parameters. We already know how to compute the effects of small additive modifications to the model parameters on the output: that's what the gradient is. So if you want to know how much each training sample contributed to the output, just compute the dot product between the two gradients. Actually doing that for a billion-parameter model would be slightly expensive because the gradients are also billion-dimensional, so you'd need to approximate the dot product via dimensionality reduction and use a vector database to filter for training samples with high approximated dot product. But I think those layers of approximations would still be better than throwing your hands up in the air and claiming you have no way to know because linear algebra is magic.