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Related Questions
- What are some strategies for adapting LLMs to low-resource languages, and how can prompt engineering play a role?
- How can prompt engineering techniques, such as data augmentation and transfer learning, be applied to improve LLM performance in low-resource languages?
- What are some common challenges encountered when training LLMs on low-resource languages, and how can prompt engineering help address these challenges?
- Can you provide examples of successful applications of prompt engineering in improving LLM performance in low-resource languages, particularly in healthcare settings?
- How can prompt engineering be used to mitigate the effects of language bias and cultural insensitivity in LLMs, which can exacerbate healthcare disparities?
- What role can multilingual LLMs play in addressing healthcare disparities, and how can prompt engineering be used to optimize their performance?
- Are there any emerging trends or techniques in prompt engineering that show promise for improving LLM performance in low-resource languages, and what are the implications for healthcare applications?
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