How does a panel of judges setup reduce LLM-as-judge bias?

A **judge panel** uses three or more LLM judges, ideally from different model families, and aggregates their scores by mean or majority vote instead of trusting one judge. The correct answer is the first option. Each model family has its own idiosyncratic biases: a GPT judge leans one way on style, a Claude judge another, a Gemini judge a third. When those quirks are not correlated across families, averaging pushes them toward zero, and disagreement between judges shrinks the random noise in the final score. The other options are wrong for instructive reasons. A panel does NOT eliminate all bias by majority vote: if every judge shares the same blind spot, the vote inherits it. Panels also do not work by handing each judge a separate quality dimension, that is a different technique called rubric decomposition. And adding judges does improve reliability through averaging, so the claim that more judges only add latency is false. The honest framing: a panel reduces variance and family-specific bias for extra cost. It does not deliver a bias-free oracle.

The correct answer is the first option. A **judge panel** of three or more models from different families averages out family-specific biases and cuts variance. The mechanism is statistical. Each family carries its own idiosyncratic tendencies. A GPT judge prefers GPT-style verbosity. A Claude judge prefers structured headers. These biases are roughly uncorrelated across families. Averaging uncorrelated errors shrinks their combined effect toward zero. Disagreement between judges also lowers variance in the aggregate score. Why the others fail. Option B is the classic trap. A majority vote does not guarantee an unbiased decision. If all judges share a blind spot, like favoring confident wrong answers, the vote inherits it. Option C describes rubric decomposition, a separate technique where each judge scores one dimension. That is not what a panel does. A panel has every judge score the same thing, then aggregates. Option D is simply wrong on the statistics. More judges DO improve reliability through averaging. The real cost is the opposite tradeoff: latency and dollars rise roughly linearly with panel size, so teams gate panels on stakes.