Marco Kuhlmann

MELD: Melding Diverse Multilingual and Multi-Domain Datasets for Named Entity Recognition Evaluation

A Systematic Comparison of Multilingual Interpretability Methods Reveals Anisotropy-Driven Failures

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.

Probing Factual Knowledge Transfer with Training Data Interventions

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.

Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training

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.