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This paper from Stony Brook University identifies 'Averaging Bias' in human faithfulness annotations for text summarization: global human labels correlate better with the average of per-sentence LLM judgments than with a strict conjunctive rule, meaning humans often label summaries as faithful even when they contain local factual errors.