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Multilingual Language Models Benefit from Imbalanced Training Data


Core Concepts
Language imbalance during training can boost cross-lingual generalization in multilingual language models, leading to better performance on less frequent languages.
Abstract
The paper investigates the impact of language imbalance on the cross-lingual generalization abilities of multilingual language models. Key highlights: In a controlled setting with perfectly equivalent "cloned" languages, the authors find that having a dominant "main" language during training significantly boosts the performance of less frequent languages. This is accompanied by stronger alignment of model representations across languages. The benefits of language imbalance are amplified with larger model sizes and longer training, to the point where a 90/10 language split outperforms a 50/50 split on both languages. The authors design training curricula that leverage language imbalance to improve performance on all languages, without modifying the training data. When extending the analysis to real languages (English and French), the authors find that lower-resource languages still benefit from higher-resource ones, but the impact of language imbalance on cross-lingual generalization is less conclusive. Longer training and larger model sizes lead to diminishing returns for the performance benefits of language imbalance on real languages. Overall, the paper provides insights into an unintuitive driver of cross-lingual generalization in multilingual language models, with implications for the design of more effective multilingual training schemes.
Stats
"When training in the 90/10 regime, we obtain a TEffEN2 of over 2" "In the 90/10 setting, we achieve better performance on both languages than under the 50/50 split." "When training a larger model with around 300M parameters (GPT medium in Languini; Stani´c et al., 2023), in the 90/10 setting, we achieve better performance on both languages than under the 50/50 split."
Quotes
"Remarkably, when training for 4.8B tokens, the 90/10 setting yields better performance in both languages, compared to the 50/50 setting." "Intriguingly, this allows trading off the performance of different languages without altering the training data."

Deeper Inquiries

How do the insights from this work apply to multilingual models with more than two languages, especially in real-world settings with diverse language families and resource levels

The insights from the study on language imbalance and its impact on cross-lingual generalization can be extended to multilingual models with more than two languages, especially in real-world settings with diverse language families and resource levels. In such scenarios, the findings suggest that introducing language imbalance during training can potentially lead to improved performance, particularly for low-resource languages. By prioritizing a dominant language or languages, the model can benefit from shared representations and circuits, enhancing its ability to generalize across multiple languages. This approach can be especially valuable in real-world settings where resource disparities among languages are common. Additionally, the study highlights the importance of considering the balance of data from different languages in multilingual model training, as language imbalance can have a significant impact on performance and generalization capabilities across diverse language families.

What are the potential downsides or risks of relying on language imbalance to improve multilingual model performance, and how can they be mitigated

While leveraging language imbalance to improve multilingual model performance can offer benefits in terms of cross-lingual generalization, there are potential downsides and risks that need to be considered. One downside is the risk of overfitting to the dominant language, which may lead to reduced performance on less frequent languages or languages with limited training data. Additionally, relying solely on language imbalance as a strategy for improving performance may not address underlying issues related to model capacity, data quality, or the complexity of language interactions. To mitigate these risks, it is essential to carefully balance the representation of different languages during training, consider the impact of model size and architecture on performance, and incorporate strategies such as data augmentation, transfer learning, or fine-tuning to enhance the model's ability to generalize effectively across diverse languages.

Given the complex interactions between language imbalance, model capacity, and training dynamics, what other factors might influence cross-lingual generalization in multilingual language models

In addition to language imbalance, model capacity, and training dynamics, several other factors can influence cross-lingual generalization in multilingual language models. One key factor is the quality and diversity of the training data available for each language. Models trained on high-quality, diverse datasets representing a wide range of linguistic features and structures are more likely to generalize effectively across languages. Another important factor is the presence of shared vocabulary elements or anchor points between languages, which can facilitate alignment and transfer of knowledge across language boundaries. Additionally, the choice of pretraining objectives, fine-tuning strategies, and evaluation metrics can impact the model's ability to generalize across languages. Considering these factors in conjunction with language imbalance, model capacity, and training dynamics can lead to more robust and effective multilingual language models with improved cross-lingual generalization capabilities.
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