You are working on a project that requires a language model to perform well across multiple languages, including low-resource languages. The model needs to be deployed on a cloud infrastructure that charges based on the number of parameters and inference time. Which approach would be the most effective for selecting an appropriate model, considering both multilingual support and cost efficiency?
Choose mBERT and perform task-specific fine-tuning
100%
Select T5-Multilingual and fine-tune on a few selected languages
0%
Use a separate smaller model for each language
0%
Use Llama 3.2 with fine-tuning on each specific language
0%

Question

You are working on a project that requires a language model to perform well across multiple languages, including low-resource languages. The model needs to be deployed on a cloud infrastructure that charges based on the number of parameters and inference time. Which approach would be the most effective for selecting an appropriate model, considering both multilingual support and cost efficiency?

Choose mBERT and perform task-specific fine-tuning

100%

Select T5-Multilingual and fine-tune on a few selected languages

0%

Use a separate smaller model for each language

0%

Use Llama 3.2 with fine-tuning on each specific language

0%

Answer

Choose mBERT and perform task-specific fine-tuning

Answer

Select T5-Multilingual and fine-tune on a few selected languages

Answer

Use a separate smaller model for each language

Answer

Use Llama 3.2 with fine-tuning on each specific language

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