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Related Questions
- What are some common data augmentation techniques used in LLMs for healthcare applications, and how can they be applied to improve model robustness?
- How does adversarial training impact the performance of LLMs in healthcare, and what are some strategies for incorporating it into model development?
- Can you provide examples of how data augmentation and adversarial training can be used to improve the generalizability of LLMs in healthcare, such as in natural language processing tasks like sentiment analysis and named entity recognition?
- What are some challenges associated with applying prompt engineering techniques to LLMs in healthcare, and how can they be addressed?
- How can LLMs be fine-tuned using healthcare-specific datasets to improve their performance and robustness in real-world applications?
- What role can transfer learning play in improving the generalizability of LLMs in healthcare, and how can it be combined with prompt engineering techniques?
- Can you discuss the potential benefits and limitations of using LLMs in healthcare, and how prompt engineering techniques can help mitigate some of the limitations?
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