In addition to the factors explored in the study, several other variables could impact the performance of multilingual language models. One crucial factor is the quality and diversity of the training data. The richness of the training data in terms of genres, dialects, and domains can significantly influence the model's ability to generalize across languages. Moreover, the quality of annotations, the presence of noise or bias in the data, and the representativeness of the training samples for each language can also affect model performance.
Another important factor is the model's fine-tuning strategy. The choice of fine-tuning techniques, the amount of fine-tuning data, and the specific tasks used for fine-tuning can all play a role in how well the model adapts to new languages. Additionally, the selection of hyperparameters, such as learning rates, batch sizes, and optimization algorithms, can impact the model's learning dynamics and generalization capabilities.
Furthermore, the architecture and design choices of the multilingual language model can influence its performance. Variations in model architectures, such as transformer-based models, recurrent neural networks, or hybrid models, can lead to differences in how well the model captures linguistic patterns and structures across languages. Additionally, the incorporation of language-specific modules, attention mechanisms, or cross-lingual alignment techniques can also impact the model's cross-lingual transfer abilities.