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Related Questions
- What are the key differences between pre-training objectives such as masked language modeling and next sentence prediction for language models?
- How does the choice of pre-training objective affect the model's ability to capture semantic meaning and contextual relationships in text?
- Can you explain the impact of fine-tuning objectives such as sequence labeling and entity recognition on the performance of a language model for text summarization?
- How does the choice of fine-tuning objective influence the model's ability to extract relevant information and generate accurate summaries?
- What are the trade-offs between using a pre-trained language model as a feature extractor versus fine-tuning the model for specific tasks like information retrieval?
- Can you discuss the importance of task-specific fine-tuning and its effect on the model's performance for text summarization and information retrieval?
- How can the choice of pre-training and fine-tuning objectives be optimized for specific domains or datasets to improve the model's performance?
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