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Related Questions
- What are the primary challenges in adapting large language models to new domains and how can label smoothing help address these challenges?
- Can label smoothing be used to improve the transferability of LLMs across different tasks and domains?
- How does label smoothing compare to other regularization techniques, such as dropout and weight decay, in terms of improving robustness in LLMs?
- What are some common pitfalls to avoid when implementing label smoothing for domain adaptation in LLMs?
- In what scenarios is label smoothing more effective than other techniques, such as data augmentation or task-specific fine-tuning?
- Can label smoothing be combined with other techniques, such as data augmentation or knowledge distillation, to further improve LLM robustness?
- What is the relationship between label smoothing and the concept of overfitting in LLMs, and how can label smoothing help prevent overfitting?
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