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Related Questions
- How do large language models handle unknown or out-of-vocabulary words in information extraction tasks?
- What are some common challenges associated with out-of-vocabulary words in information extraction, and how can they be addressed?
- Can you explain the impact of out-of-vocabulary words on the accuracy and performance of information extraction models?
- What techniques can be used to mitigate the effects of out-of-vocabulary words in information extraction, such as subword modeling or character-level encoding?
- How do different information extraction tasks, such as named entity recognition or relation extraction, handle out-of-vocabulary words differently?
- What role does pre-training and fine-tuning play in addressing out-of-vocabulary words in information extraction, and what are some best practices for pre-training and fine-tuning models?
- Can you discuss some real-world applications or use cases where handling out-of-vocabulary words is critical for the success of an information extraction task?
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