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Related Questions
- What are the most significant obstacles to improving the generalizability of large language models through scaled-up pre-training?
- How do the number of training samples and complexity of tasks impact the scalability of pre-training for improving generalizability?
- Can you discuss some of the computational and time complexity issues that arise when scaling up pre-training to improve generalizability?
- What are the key strategies for mitigating overfitting and model degradation when scaling up pre-training to improve generalizability?
- What are the implications of hardware constraints and resource limitations for scaling up pre-training for improved generalizability?
- How do training algorithms and model architectures evolve to handle the increased demand of scaling up pre-training for improved generalizability?
- What can be learned from existing solutions for scaled-up pre-training to improve generalizability in specific domains or areas of NLP?
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