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CLLMs achieve 99.0% accuracy on OpenBookQA while maintaining low calibration error, redefining how we handle uncertainty in LLMs.
Second-order uncertainty representations matter: set-based and distribution-based methods, often considered incomparable, can be rigorously compared, revealing how representation choices impact uncertainty-aware performance.