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Related Questions
- What are the key differences between pre-training and fine-tuning in the context of large language models for multi-document summarization?
- How does pre-training a large language model on a general corpus impact its performance on specific summarization tasks?
- Can you explain the importance of fine-tuning a pre-trained model on a specific summarization dataset for achieving optimal performance?
- What are some common techniques used for fine-tuning large language models for multi-document summarization tasks?
- How does the choice of pre-training objective (e.g. masked language modeling, next sentence prediction) affect the performance of the model on summarization tasks?
- What are some challenges associated with fine-tuning large language models for multi-document summarization, and how can they be addressed?
- Can you discuss the trade-offs between pre-training and fine-tuning in terms of computational resources and model performance?
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