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A data science team needs to build a system to categorize user reviews written in Japanese as either 'Positive' or 'Negative'. The team has access to a large, pre-trained multilingual model and a comprehensive dataset of labeled user reviews in English, but they have no labeled data in Japanese. Arrange the steps below to correctly describe the cross-lingual workflow they should follow.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
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A development team has successfully fine-tuned a large, multilingual foundational model to classify customer support tickets in English into categories like 'Billing Issue', 'Technical Problem', and 'General Inquiry'. They used a large, labeled English dataset for this process. The company is now expanding to Germany and wants to apply the same classification to incoming German support tickets. However, they have no labeled German data and no budget for translation or new labeling. Which of the following strategies represents the most effective and direct use of their existing model for the new German tickets?
Evaluating a Cross-Lingual Model's Performance
A data science team needs to build a system to categorize user reviews written in Japanese as either 'Positive' or 'Negative'. The team has access to a large, pre-trained multilingual model and a comprehensive dataset of labeled user reviews in English, but they have no labeled data in Japanese. Arrange the steps below to correctly describe the cross-lingual workflow they should follow.