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RaDiCal reveals that relying solely on attention saliency can misguide token pruning, leading to a 39-45% reduction in FLOPs while enhancing ranking performance across key datasets.
Transforming retrieval failures into hard pairs can boost model performance while using 26% to 75% fewer synthetic samples than conventional methods.
Safety in education-facing LLMs is inversely related to their teaching effectiveness, challenging assumptions about model specialization.
