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Traditional accuracy metrics can obscure critical improvements in reasoning fluency, as shown by fine-tuned models that reason correctly in low-resource languages despite initial benchmark null results.
A parameter-free BM25 baseline outperforms advanced multilingual models in retrieving Modern Greek data, revealing the untapped potential of tailored approaches for niche languages.
Sophea-Genesis-1 not only excels in inflectional morphology but also challenges the notion that larger models are inherently better at language tasks.
