  In this paper, we present our team's submissions for SemEval-2024 Task-6 -
SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration
Mistakes. The participants were asked to perform binary classification to
identify cases of fluent overgeneration hallucinations. Our experimentation
included fine-tuning a pre-trained model on hallucination detection and a
Natural Language Inference (NLI) model. The most successful strategy involved
creating an ensemble of these models, resulting in accuracy rates of 77.8% and
79.9% on model-agnostic and model-aware datasets respectively, outperforming
the organizers' baseline and achieving notable results when contrasted with the
top-performing results in the competition, which reported accuracies of 84.7%
and 81.3% correspondingly.
