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On HealMed, performance declined most in low-resource languages, although the size of the gap varied markedly across languages and models, whereas many open-source and medically specialized models showed larger and less consistent gaps.
This study establishes a reliable approach of data generation, training, and benchmarking, paving the way toward further bootstrapping the quality of many-to-many translation for programming languages.
Looped Mamba and Looped Hybrid Mamba-Transformer architectures, which repeatedly apply a shared Mamba block to introduce explicit finite-depth recurrent computation, are investigated and adaptive exit-state selection improves downstream performance at intermediate depths, while actual inference-time savings require add...
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