Cross-lingual transfer allows models to perform tasks in languages unseen during training and is often assumed to benefit from increased multilinguality.In this work, we challenge this assumption in the context of two underexplored, sense-aware tasks: polysemy disambiguation and lexical semantic change.Through a largescale analysis across 28 languages, we show that multilingual training is neither necessary nor inherently beneficial for effective transfer.Instead, we find that confounding factors, such as fine-tuning data composition and evaluation artifacts, can better account for the perceived advantages of multilinguality.Our findings call for more rigorous evaluations in multilingual NLP, and more nuanced and sensible choice of models for transfer.We release fine-tuned models and benchmarks to support further research, with implications extending to low-resource and typologically diverse languages.

The task of Definition Generation has recently gained attention as an interpretable approach to modeling word meaning.Thus far, most research has been conducted in English, with limited work and resources for other languages.In this work, we expand Definition Generation beyond English to a suite of 22 languages and evaluate Llama-based models within a monolingual, multilingual, and cross-lingual setting.Our experiments show that monolingual fine-tuning consistently outperforms pretrained baselines, with the largest gains observed in languages with lower initial performance; and that multilingual fine-tuning does not consistently improve performance on the individual fine-tuning languages.Our cross-lingual evaluation reveals that models fine-tuned on a single language typically lose the ability to generate definitions in other languages, whereas multilingual models exhibit robust generalization even to languages unseen during fine-tuning.