When do benchmark results become more sensitive to luck?
Question: Answer in one to three sentences using the relationship described here.
Sample answer: Results are more sensitive to luck when the development data and the final evaluation data come from noticeably different distributions. If both sets come from the same distribution, random chance has less influence on the reported performance.
Key points:
- Different development and evaluation distributions
- Greater influence of chance
- Contrast with the same-distribution case
Rubric: The answer should explain that a mismatch between development and evaluation distributions increases the role of luck compared with using sets drawn from the same distribution.
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