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Related Questions
- What is the primary goal of active learning in the context of fine-tuning large language models?
- How can I determine the uncertainty of my LLM's predictions to identify the most informative data points for active learning?
- What are some common methods for selecting the most informative data points, such as uncertainty sampling, query-by-committee, or expected model change?
- How can I use active learning to adapt my LLM to a specific task or domain, such as sentiment analysis or question answering?
- What are some challenges and limitations of active learning in fine-tuning large language models, such as data quality or labeling issues?
- Can I use active learning to reduce the amount of labeled data required for fine-tuning, and if so, how?
- How can I evaluate the effectiveness of active learning in improving the performance of my LLM on a specific task?
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