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Only ILO's correlation with cross-lingual transfer (Spearman's $\rho = 0.90$) survives controls for model size, family, and per-task variation and is recommended as the primary sharing metric to be reported alongside anisotropy diagnostics.
The results show that fact transfer is very limited: under the strictest removal condition, a large majority of English-acquired facts fail to transfer into Persian, and it is shown that sentence-level co-occurrence removal is insufficient to eliminate fact signal.
This work investigates continued pre-training for adapting large language models to Swedish journalism, using a high-quality dataset that is curate from millions of news articles and demonstrates the importance of targeted evaluation in the adaptation process.
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