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Related Questions
- What are some common challenges when fine-tuning a pre-trained language model for a new task?
- How can data scarcity affect the performance of a large language model?
- What are some strategies for transferring knowledge from a pre-trained model to a new task?
- What are some limitations of fine-tuning a pre-trained model versus training from scratch?
- Can you explain the concept of catastrophic forgetting in the context of LLMs and how to mitigate it?
- How does the quality and quantity of training data impact the performance of a large language model?
- What are some techniques for adapting a pre-trained model to a new domain or task with limited data?
- Can you discuss the role of prompt engineering in adapting LLMs to new tasks and how it can be optimized?
- What are some common issues with overfitting and underfitting in LLMs, and how can they be addressed?
- How can multi-task learning be used to adapt LLMs to multiple tasks simultaneously?
- What are some methods for evaluating the performance of a large language model on a new task?
- Can you explain the concept of pseudo-labeling and its application in adapting LLMs to new tasks?
- What are some strategies for handling out-of-distribution data in LLMs and how to improve robustness?
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