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Related Questions
- What is the impact of using a larger pre-training dataset on a language model's ability to generalize to unseen tasks?
- How does the diversity of tasks used in pre-training affect a language model's performance on tasks that require specific domains or formats?
- Can a language model that is pre-trained on a wide range of tasks learn to adapt to new tasks more quickly than one that is pre-trained on a narrower range of tasks?
- What is the effect of using a combination of tasks, such as language translation and question-answering, on a language model's performance compared to using a single task?
- How does the number of tasks used in pre-training affect a language model's ability to capture nuances of language, such as idioms and colloquialisms?
- What is the relationship between the type of tasks used in pre-training and a language model's ability to handle out-of-distribution inputs, such as unseen words or domains?
- Can a language model that is pre-trained on a variety of tasks learn to recognize and mitigate the effects of task-specific biases, such as confirmation bias?
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