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Related Questions
- How do the shared and task-specific representations in LLMs impact their ability to generalize to new tasks and domains?
- What are the implications of learning multiple tasks simultaneously on the LLM's capacity to adapt to unseen contexts and relationships?
- Can you explain how the contextual relationships learned in multi-task learning help or hinder the LLM's performance on out-of-distribution tasks?
- How does the type and number of tasks learned affect the LLM's ability to capture generalizable patterns and relationships?
- What role do shared and task-specific embeddings play in facilitating or impeding the LLM's ability to generalize to new contexts?
- Can you discuss the trade-offs between learning multiple tasks simultaneously and the potential for overfitting or underfitting on out-of-distribution tasks?
- How do the contextual relationships learned in multi-task learning compare to those learned in single-task learning in terms of their ability to generalize to new tasks and domains?
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