Ali Hamdi

Fine-tuning LLMs can dramatically boost answer extraction accuracy, achieving a ROUGE-L score of 86.84% in complex question answering scenarios.

Class imbalance cripples language model evaluation, but CAMO, a new ensemble method, dynamically boosts minority classes to achieve state-of-the-art macro F1-scores on imbalanced datasets.