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Related Questions
- What are the most common pre-training tasks used to capture domain-specific knowledge in large language models?
- How does the selection of pre-training tasks impact the model's ability to generalize to out-of-domain data?
- What are some effective strategies for fine-tuning a pre-trained model to adapt to a specific domain?
- Can you explain the concept of 'domain knowledge' in the context of large language models and how it is captured during pre-training?
- How does the choice of pre-training task influence the model's ability to recognize and utilize domain-specific entities, such as jargon and terminology?
- What are the potential risks of overfitting to a specific domain during pre-training, and how can they be mitigated?
- Can you discuss the relationship between pre-training task choice and the model's ability to capture nuanced, implicit knowledge in a domain?
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