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arXiv2h ago
4.3

CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data

Mohamed Ehab (Faculty of Computer Science, October University for Modern Science & Arts, Giza, Egypt), Ali Hamdi (Faculty of Computer Science, October University for Modern Science & Arts, Giza, Egypt), Khaled Shaban (Department of Computer Science and Engineering, Qatar University, Doha, Qatar)

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Analysis

Viral velocity
low
Implementation gapYES
Novelty5/10
Categorypaper
Topics
fine-tuningevaluationimbalance

Opportunity Brief

Create an ensemble wrapper for LLM evaluation that specifically corrects for minority class bias. This tool would be invaluable for developers testing safety-critical classification tasks with sparse positive samples.

Suggested repo: camo-ensemble

"Eliminate the minority class penalty in your LLM benchmarks."

Estimated effort: 40h