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SpanUQ reveals that span-level uncertainty quantification can enhance both the accuracy and efficiency of LLM outputs, outperforming existing methods by a factor of 10-20x.
Transforming failures into focused training tasks boosts tool-using language model performance by over 8% on key benchmarks.
Finally, a standardized benchmark to rigorously evaluate how well models generalize carbon flux predictions to geographically distinct ecosystems they've never seen before.
