For SemEval-2026 Task 11 (Subtask 1: English), Team Ellat investigates whether language models can assess logical validity independently of semantic plausibility. Since these models learn statistical patterns instead of explicit logical rules, they often rely on world knowledge and semantic shortcuts rather than formal logic. To address this challenge, we evaluate three architectures: MiniLM-L6-mnli-binary, DeBERTa-v3-small, and Llama 3.1-8B-Instruct, applying task-specific fine-tuning for encoder models and Abstract Logic Augmentation with QLoRA for LLaMA. DeBERTa achieved the strongest overall performance, MiniLM showed clear reductions in content bias after fine-tuning, and Llama 3.1-8B exhibited strong plausibility bias in the zero-shot setting. However, our augmented fine-tuning approach led to only modest improvements and a partial shift toward structure-based reasoning. Overall, fine-tuning and abstraction-based augmentation reduce plausibility bias, but fully separating logical validity from semantic content remains challenging across architectures.