Automatic LLM rankers align well with humans on broad leaderboards but degrade sharply when ranking close-performing models, and instance-level judge accuracy does not predict system-level bencher quality.
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Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference
Automatic LLM rankers align well with humans on broad leaderboards but degrade sharply when ranking close-performing models, and instance-level judge accuracy does not predict system-level bencher quality.