Are trained LLM's composable in any way? Like if you and I trust 99% of the same data, but each have 1% where we disagree, must we have two entirely separate m

Are trained LLM's composable in any way? Like if you and I trust 99% of the same data, but each have 1% where we disagree, must we have two entirely separate models, or can we pool compute in the 99% case (along with the others who agree) and then create a derivative model for ourselves which covers for the differences in our trust models? I have only a rudimentary understanding of neural nets but it doesn't seem crazy that the weights could be manipulated in such a way while preserving the utility of the model. I ask because I think it would be useful to know which statements two LLMs of equal power agree on and which they disagree on. You could then map that backwards to differences in their training data (only feasible if the differences are small). If instead two LLMs of equal power represent a missed opportunity to have one of greater power, and the disagreement analysis is prohibitively expensive to do, then that's a bit of a different world.