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Related Questions
- What is the concept of calibration in the context of large language models (LLMs)?
- How do LLMs handle out-of-distribution (OOD) data, and what is the role of calibration in this process?
- Can you explain the difference between calibration and confidence in the context of LLMs and OOD data?
- How does calibration impact the performance of LLMs on OOD data, and what are the implications for real-world applications?
- What are some common methods for calibrating LLMs on OOD data, and what are their strengths and weaknesses?
- Can you provide examples of how calibration can be used to improve the reliability of LLMs on OOD data?
- How does the concept of calibration relate to other areas of machine learning, such as overfitting and regularization?
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