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Related Questions
- What is the concept of 'shifting the distribution' in A/B testing, and how does it impact the evaluation of large language models (LLMs)?
- Can you provide examples of scenarios where shifting the distribution occurs in A/B testing, and how it affects the results of LLM evaluation?
- How can data scientists mitigate the effects of shifting the distribution in A/B testing to ensure accurate evaluation of LLMs?
- What are the implications of shifting the distribution on the reliability of LLM evaluation metrics, such as accuracy and F1-score?
- Can you explain how shifting the distribution affects the comparison of LLMs across different tasks and datasets?
- What role does data preprocessing play in addressing the issue of shifting the distribution in A/B testing for LLM evaluation?
- How can the use of control groups and baselines help to account for shifting distributions in A/B testing for LLM evaluation?
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