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Frontier model performance is now within reach for a wider array of institutions, thanks to a novel Continual Learning approach that minimizes forgetting while maximizing capability.
Frontier LLMs struggle with contract scrubbing, achieving only modest recall rates despite their prowess in general benchmarks.
No current LLM can accurately identify missing legal information in user queries, with all evaluated models struggling to balance responses to both deficient and complete questions.
LLM agents in high-stakes domains can be verified more reliably by accumulating evidence grounded in expert guidelines, achieving a 12% AUROC improvement and 50% Brier score reduction over existing methods.
