A new arXiv paper investigates the reliability of using agreement among Large Language Models (LLMs) as a proxy for correctness. The study, which involved 53 different LLM runners and 265,000 samples, found that while agreement can be a weak positive predictor of accuracy, it is not a standalone confidence score. The research highlights that models can agree due to shared biases or memorized heuristics rather than factual accuracy, particularly noting that frontier models exhibit over-confidence with recurring errors. The findings suggest that self-consistency is a conditional indicator of correctness, best utilized for allocating compute resources rather than as a definitive measure of accuracy. AI
IMPACT Highlights limitations in current LLM evaluation methods, suggesting a need for more robust confidence scoring beyond simple agreement.
RANK_REASON Academic paper published on arXiv discussing LLM evaluation methods. [lever_c_demoted from research: ic=1 ai=1.0]
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