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Related Questions
- What are some common data quality issues that can lead to poor performance in domain-specific fine-tuning?
- How can catastrophic forgetting occur in fine-tuning large language models on specific domains?
- What are some strategies to prevent overfitting during fine-tuning on smaller datasets?
- Why is it essential to assess the model's performance on out-of-domain data when fine-tuning on a specific domain?
- What are some techniques for handling domain adaptation, where the model is fine-tuned on a specific domain, but needs to generalize to other related domains?
- How can we tackle the issue of data annotation bias when fine-tuning on a specific domain, where the annotation data reflects the biases of the authors?
- What is the role of few-shot learning in fine-tuning large language models on specific domains, and how does it differ from traditional domain adaptation methods?
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