Originally published on AI Tech Connect.
What you need to know Two teams are choosing a model. A Bengaluru fintech needs to extract obligations from vendor contracts and answer customer queries in Hindi and Tamil as well as English. A Manchester health-tech company needs to triage clinical correspondence under UK information-governance constraints. Both start the same way: open the two candidate vendors' release posts, compare the benchmark tables, pick the higher number. That process is more fragile than it looks, and one of the biggest reasons is data contamination — test items leaking into the training data. When an evaluation item has already appeared somewhere in a model's pre-training corpus, the model can answer it partly from memory rather than by genuinely generalising, and the reported score goes up without the…
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