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The article discusses how to choose which answer to trust when multiple AI models give conflicting results and suggests methods such as checking sources or seeking domain expertise.
The paper proposes CASE, a dynamic selection combiner using a decodability criterion to predict when hidden-state selection outperforms majority voting in large language models, enhancing reliability on difficult questions.