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Related Questions
- What are the key differences between fine-tuning and adapter-based transfer learning for low-resource languages?
- How can pre-trained language models be adapted for tasks such as text classification, sentiment analysis, and named entity recognition in low-resource languages?
- What are the challenges of fine-tuning pre-trained LLMs for low-resource languages, and how can they be addressed?
- What are some common evaluation metrics used to measure the performance of pre-trained LLMs adapted for low-resource languages?
- Can you explain the concept of domain adaptation in transfer learning and how it applies to low-resource languages?
- How does the choice of pre-training corpus and architecture affect the performance of LLMs adapted for low-resource languages?
- What are some successful applications of transfer learning for low-resource languages, such as machine translation and language modeling?
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