AI Research
Agreement Overstates Evidence: Error Dependence in LLM Judge Consensus
Why consensus among AI models is not a reliable indicator of accuracy in automated decision-making.
Consensus among LLM judges is often taken as strong evidence that a decision is correct. This assumes that judges make their errors independently. In practice, LLM judges are often trained and evaluated in similar ways, so they can make the same mistakes. We study how this depend...
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ArXiv CS.AI
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ArXiv CS.AI
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The reliance on LLM consensus as a proxy for ground truth is fundamentally flawed due to shared architectural and training biases. This research exposes how correlated error patterns create an illusion of accuracy that could lead to widespread systemic failures in automated evaluation.
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