This is basically another argument that deep learning works only as a [generative] information retrieval - i.e a stochastic parrot, due to the fact that the training data is a very lossy representation of the underlying domain.
Because the data/labels of genes do not always represent the underlying domain (biology) perfectly, the output can be false/invalid/nonsensical.
in cases where it works very well - there is data leakage, because by design LLMs are information retrieval tools. It comes form the information theory standpoint, a fundamental "unknown unknown" for any model.
my takeaway is that its not a fault of the algorithm, its more the fault of the training dataset.
We humans operate fluidly in the domain of natural language, and even a kid can read and evaluate whether text make sense or not - this explains the success of models trained on NLP.
but in domains where training data represents the fundamental domain with losses, it will be imperfect.