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Research on large-scale multilingual models, such as XLM-like architectures, has revealed important scaling principles. As the number of languages the model must support grows, a corresponding increase in model size is necessary for effective performance. Similarly, a larger shared vocabulary is beneficial to accommodate the linguistic diversity of an expanded set of languages.

Scaling Considerations for Multilingual Models

Analyze the two proposals below. Which proposal is more likely to result in a high-performing model across all 50 languages, and what is the primary risk associated with Proposal Alpha's strategy?

Multilingual Model Development Strategy

A machine learning team is expanding their multilingual language model from supporting 20 languages to over 100. A junior engineer suggests that to save resources, they should keep the model's parameter count and shared vocabulary size the same as the original model. Analyze the potential negative consequences of this approach and explain the underlying reasons why both model size and vocabulary size generally need to increase with the number of supported languages.

Justifying Scaling Decisions in Multilingual Model Development

A research lab has a successful multilingual model that performs well on 10 distinct languages. The team is now tasked with building a new version to support 100 languages. To manage computational costs, they propose keeping the new model's parameter count (size) and shared vocabulary size identical to the original 10-language model. Based on established scaling principles for such models, what is the most likely outcome of this strategy?

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