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This paper proposes RoPoLL, a robust panel of LLM judges that replaces standard averaging with geometric median aggregation to handle biased contamination from individual judges, providing theoretical guarantees and empirical gains over standard PoLL.
This simulation study examines the double descent phenomenon for least-squares interpolation on contaminated data in linear regression, comparing the performance of the least-squares interpolator with robust alternatives.