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Concept recoverability in AI grading systems varies significantly by architecture, revealing hidden biases that could undermine assessment fairness.
LLMs aren't equally reliable as NLG evaluators, but a Bradley-Terry extension called BT-sigma can learn judge reliability from pairwise comparisons alone, improving ranking accuracy without human supervision.
