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Related Questions
- How does domain-specific training data impact the performance of LLMs on out-of-distribution tasks?
- What are the benefits and drawbacks of fine-tuning LLMs on specific domains versus training from scratch?
- Can LLMs generalize to new tasks and domains without being explicitly trained on that data?
- How does the quality and quantity of training data affect the ability of LLMs to generalize to new tasks and domains?
- What are some techniques to improve the generalizability of LLMs across different domains and tasks?
- How does the trade-off between domain-specific training data and generalizability impact the development of LLMs for real-world applications?
- Can LLMs learn to adapt to new domains and tasks through self-supervised learning or other unsupervised methods?
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