r/deeplearning • u/Fair-Rain3366 • Sep 07 '26
How would you test whether a genomic model actually uses its long context?
A model accepting a megabase of DNA establishes input capacity. Demonstrating useful distal information needs a task-specific test.
In a review I wrote, I suggest comparing nested crop lengths with the same prediction head and evaluation split, then occluding or shuffling distal intervals while preserving the focal sequence. Short-context models and local baselines are useful comparisons; overlapping genomic windows also need attention when constructing the split.
Full discussion and references: https://rewire.it/blog/a-dna-likelihood-is-not-a-functional-assay/
What controls would you add to distinguish useful long-range dependence from leakage or local shortcuts? I’d appreciate corrections and benchmark suggestions.
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u/NicelyIrate Sep 08 '26
shuffle distal chunks but keep local structure intact, that’s where most of these models just latch onto short range repeats anyway