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.
@article{arxiv.2404.01210,
title = {AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis},
author = {Natalia Grigoriadou and Maria Lymperaiou and Giorgos Filandrianos and Giorgos Stamou},
journal= {arXiv preprint arXiv:2404.01210},
year = {2024}
}