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In LLM-as-a-judge evaluation, the judge's judgment is highly dependent on the wording of the prompt, and different descriptions of evaluation criteria can cause the pass rate to plummet from 100% to 0%
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10/3/2026
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UnverifiedThe LLM judge serving as evaluator assigns almost all outputs a score of 7 or 8, causing it to lose discriminative power.76% similarUnverifiedResearch shows that in certain tasks, simply changing '请判断' to '请分析' in a prompt can cause accuracy fluctuations of 5–10 percentage points.67% similarUnverifiedThe core of evaluation is scoring, typically expressing the quality of results using a continuous value from 0 to 1.66% similarUnverified评估基准测试质量的关键指标是假阳性率和假阴性率，理想基准应将两者控制在较低水平65% similarUnverifiedEvaluations such as LLM-as-a-Judge incur double or even multiple times the token overhead, because tokens are consumed both to generate the answer being evaluated and to have another model score it.65% similar
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