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Text-anchored relational extraction eliminates the costly trade-off between claim verification and token-level error localization, matching state-of-the-art factuality detection at a fraction of the inference overhead.
Procedural memory boosts LLM performance in workplace tasks, with some skills achieving over 73% accuracy across different models while others falter under transfer.
Forget hand-crafted features: this system uses an LLM to automatically discover features from event sequences that outperform even state-of-the-art embeddings by up to 5.8%.
LLMs can now achieve state-of-the-art performance in transaction analytics by grounding them with a retrieval-augmented knowledge base of behavioral patterns derived from financial transactions.