Large language models used as judges in evaluation processes exhibit a significant "position bias," where they favor answers presented earlier in a list, regardless of content quality. This bias, along with verbosity and self-preference biases, can lead to inaccurate assessments. To mitigate this, an adversarial panel approach is proposed, involving multiple diverse LLM judges and systematic rotation of answer positions to ensure that scores reflect content rather than presentation order. AI
IMPACT Evaluation metrics for LLMs may be unreliable due to systematic biases, necessitating new methodologies for accurate assessment.
RANK_REASON The item describes a research finding about the behavior of LLMs in evaluation tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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