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Related Questions
- What is the primary goal of active learning in the context of large language models (LLMs)?
- How does active learning help LLMs adapt to new domains and improve performance on out-of-domain data?
- Can you explain the difference between active learning and traditional supervised learning in the context of LLMs?
- What are some common techniques used in active learning to select the most informative data points for LLMs to learn from?
- How does active learning help LLMs reduce the risk of overfitting to a specific domain and improve generalizability?
- What are some real-world applications of active learning in improving LLMs' performance on out-of-domain data?
- Can you discuss the trade-offs between the computational cost of active learning and the potential benefits of improved performance on out-of-domain data?
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