Causes of Cross-Linguistic Performance Disparities

Determine whether performance disparities across languages in multilingual language models reflect model biases, data biases, or specific linguistic properties.

Background

The paper situates structural transfer within multilingual language modeling, where models must learn from heterogeneous languages with unequal data distributions. Performance differences across languages may result from properties of the model or training data rather than from inherent linguistic differences. Resolving this question would clarify how to interpret cross-lingual evaluation disparities and how to design more effective multilingual training regimes.

References

An open question is whether performance disparities across languages reflect model or data biases, or instead arise from specific linguistic properties .

— Structural priors for data-efficient language learning  (2609.11505 - Veitsman et al., 10 Sep 2026) in Section 2, paragraph “Multilingual modeling”

It remains an unanswered question which properties of the structural source interact with the target language data and how they influence the performance on the evaluation task.

— Structural priors for data-efficient language learning  (2609.11505 - Veitsman et al., 10 Sep 2026) in Section 2, paragraph “Empirical findings”

Whether the decodability–causality separation and the category-specificity of bias features hold in typologically diverse or lower-resource languages remains unexamined.

— Tracing Stereotypes from Representation to Output in Multilingual LLMs  (2609.08322 - Tumurchuluun et al., 8 Sep 2026) in Limitations section