While the top-performing models demonstrate excellent calibration between their knowledge claims and web presence data, there's significant variation across the model landscape. The gap between best and worst performers reveals important insights about knowledge calibration challenges in language models.
Models with poor web alignment correlation tend to exhibit overconfidence about obscure species or inappropriate uncertainty about well-documented ones. This misalignment suggests these models may have learned spurious patterns during training or lack proper calibration mechanisms. The following visualization shows models that need the most improvement in aligning their confidence with real-world information availability.