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Related Questions
- What are some techniques for fine-tuning large language models for named entity recognition in text summarization?
- How can the use of contextualized embeddings, such as BERT or RoBERTa, improve named entity recognition in text summarization?
- What are some strategies for handling out-of-vocabulary words and named entities in large language models for text summarization?
- Can you discuss the impact of pre-training and fine-tuning on the performance of large language models for named entity recognition in text summarization?
- How can the use of attention mechanisms and hierarchical architectures improve the performance of large language models for named entity recognition in text summarization?
- What are some common challenges and limitations of using large language models for named entity recognition in text summarization, and how can they be addressed?
- Can you explain the role of entity linking and disambiguation in improving the performance of large language models for named entity recognition in text summarization?
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