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Most LLM lie detectors fail when models adopt anti-factual personas, instead latching onto spurious correlations like instruction following.
The results support the conclusion: foundation-model supervision can amplify a fixed manual annotation budget into a substantially larger training set and yield a compact, deployable edge model.
