  In this paper, we present our novel systems developed for the SemEval-2024
hallucination detection task. Our investigation spans a range of strategies to
compare model predictions with reference standards, encompassing diverse
baselines, the refinement of pre-trained encoders through supervised learning,
and an ensemble approaches utilizing several high-performing models. Through
these explorations, we introduce three distinct methods that exhibit strong
performance metrics. To amplify our training data, we generate additional
training samples from unlabelled training subset. Furthermore, we provide a
detailed comparative analysis of our approaches. Notably, our premier method
achieved a commendable 9th place in the competition's model-agnostic track and
17th place in model-aware track, highlighting its effectiveness and potential.
