7 papers · 1 filter
CAMO: A Class-Aware Minority-Optimized Ensemble for Robust Language Model Evaluation on Imbalanced Data
Mohamed Ehab, Ali Hamdi, Khaled Shaban
Real-world categorization is severely hampered by class imbalance because traditional ensembles favor majority classes, which lowers minority performance and overall F1-score. We p…
CMHL: Contrastive Multi-Head Learning for Emotionally Consistent Text Classification
Menna Elgabry, Ali Hamdi, Khaled Shaban
Textual Emotion Classification (TEC) is one of the most difficult NLP tasks. State of the art approaches rely on Large language models (LLMs) and multi-model ensembles. In this stu…
Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records
Abdulrahman Allam, Seif Ahmed, Ali Hamdi +1
The development of medical chatbots in Arabic is significantly constrained by the scarcity of large-scale, high-quality annotated datasets. While prior efforts compiled a dataset o…
MSLEF: Multi-Segment LLM Ensemble Finetuning in Recruitment
Omar Walid, Mohamed T. Younes, Khaled Shaban +2
This paper presents MSLEF, a multi-segment ensemble framework that employs LLM fine-tuning to enhance resume parsing in recruitment automation. It integrates fine-tuned Large Langu…
Augmented Fine-Tuned LLMs for Enhanced Recruitment Automation
Mohamed T. Younes, Omar Walid, Khaled Shaban +2
This paper presents a novel approach to recruitment automation. Large Language Models (LLMs) were fine-tuned to improve accuracy and efficiency. Building upon our previous work on…
Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction
Mohamed Basem, Islam Oshallah, Ali Hamdi +2
Quranic Question Answering presents unique challenges due to the linguistic complexity of Classical Arabic and the semantic richness of religious texts. In this paper, we propose a…