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Related Questions
- Can you explain how data augmentation techniques can be used to increase the size and diversity of training data for LLMs in low-resource languages?
- How can transfer learning be applied to adapt pre-trained LLMs to low-resource languages, and what are the benefits and challenges of this approach?
- What are some strategies for leveraging multilingual datasets to improve LLM performance in low-resource languages?
- Can you discuss the role of prompt engineering in developing more effective prompts for LLMs in low-resource languages, and how this can impact performance?
- How can LLMs be fine-tuned for specific low-resource languages using techniques such as masked language modeling and next sentence prediction?
- What are some common pitfalls to avoid when applying prompt engineering techniques to LLMs in low-resource languages, and how can these be mitigated?
- Can you provide examples of successful applications of prompt engineering and transfer learning in low-resource languages, and what lessons can be learned from these examples?
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