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Related Questions
- How does pre-training on diverse datasets impact the ability of language models to handle unseen words and phrases?
- Can you explain the concept of 'contextual understanding' in the context of language models and its relation to out-of-vocabulary words?
- What are some strategies for fine-tuning pre-trained language models to improve their performance on specific tasks and domains?
- How does the size and diversity of the pre-training dataset affect the language model's ability to generalize to new, unseen text?
- What are some common pitfalls or challenges associated with using pre-trained language models on out-of-vocabulary words and phrases?
- Can you discuss the trade-offs between pre-training on large datasets and fine-tuning on smaller, task-specific datasets?
- How can developers use techniques like subword modeling or character-level modeling to improve the robustness of language models to out-of-vocabulary words and phrases?
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