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Evaluation reference choices can drastically alter model performance and rankings, challenging the reliability of current CXR machine learning assessments.
Multilingual LLM evaluators can misjudge content, favoring lower-resource languages and potentially allowing harmful material to slip through safety filters despite high accuracy scores.
