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University of Bonn
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Achieving up to 9 percentage points improvement in latency prediction accuracy, HiFi-LLP revolutionizes hardware-aware neural architecture search efficiency.
English LLMs dominate math reasoning with a richer set of parameters, while lower-resource languages struggle with significant gaps.
LLMs can get a free performance boost: decoupling compute and capacity within each layer lets you beat standard transformers at the same FLOPs.
Looping helps transformers think harder on math problems, while memory lets them remember more commonsense facts, and combining both beats simply scaling up layers.
