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LLM-generated reviews can boost paper scores by up to 35% when authors strategically revise based on feedback, but alignment with human reviews is inconsistent.
LLM ensembles excel at classifying narrative similarity, but simpler embedding models can achieve comparable performance with clever pre- and post-processing.
Injecting XML annotation tags directly into machine translation surprisingly boosts both translation quality and cross-lingual transfer performance, achieving up to +39.9 F1 on NER.