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Related Questions
- How is pre-training used in LLMs to acquire universal knowledge and language understanding prior to fine-tuning?
- What is the objective of fine-tuning language models, and how is it applied to specific task-oriented applications?
- Why is pre-training followed by fine-tuning in multilingual LLMs for effective domain adaptation and minimal task-specific data?
- How does pre-training for masked language modeling facilitate linguistic understanding and context-specific encoding in LLMs during fine-tuning?
- What is the difference in pre-training objectives for named entity recognition (NER), sentiment analysis, and intention detection in fine-tuning?
- How can overfitting be prevented through careful pre-training and fine-tuning of language model parameters, especially with diverse task-specific datasets?
- What is the effect on model performance when using small, high-quality task-oriented datasets for fine-tuning, as opposed to the use of large-scale dataset for pre-training and model adaptation?
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