3 papers
cs.CL2026
Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs
Nagham Omar, Mahmoud Jabarin, Maya Rozenshtein +3
Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed in different ways. Existing wor…
cs.CL2026
It's Not What the Image Shows: Irrelevant Context Destabilises VLM Judges Without Informing Them
Nagham Omar, Mahmoud Jabarin, Kinan Ibraheem +1
Vision-language models (VLMs) are increasingly used in place of human annotators, making it important that substitutability tests reflect the model rather than incidental evaluatio…
cs.LG2026
Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning
Nagham Omar, Maya Rozenshtein, Evgeny Mishlyakov +1
Training strategy, namely whether to retrain from scratch or fine-tune from the previous checkpoint, is an overlooked decision variable in active learning. We show that this choice…