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Related Questions
- Can large language models learn to recognize and handle out-of-vocabulary words in text summarization tasks?
- How do large language models handle named entity recognition in text summarization, especially when the entities are not well-represented in the training data?
- What are some strategies for improving the performance of large language models on named entity recognition in text summarization?
- Can large language models be fine-tuned for specific domains or topics to improve their performance on named entity recognition in text summarization?
- How do large language models handle ambiguity in named entity recognition, such as homographs or homophones?
- Can large language models be used for multi-document summarization, and how do they handle named entity recognition in such cases?
- What are some common techniques used to handle out-of-vocabulary words in large language models, and how do they impact the performance of text summarization tasks?
- How do large language models learn to recognize and extract relevant entities from unstructured text data in text summarization?
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