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Related Questions
- What are some common issues that arise when using pre-trained models on out-of-distribution data?
- Can suboptimal tokenization lead to biased language modeling outcomes?
- How does tokenization influence model generalizability in the context of domain shifts?
- In what ways can inadequate tokenization affect model performance on edge cases or rare events?
- What techniques can be employed to adapt tokenization strategies for effective out-of-distribution data handling?
- How does suboptimal tokenization impact the transferability of learned representations?
- Can the effects of suboptimal tokenization be mitigated through more robust preprocessing methods or model architectures?
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