AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis
<|reference_start|>AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis: 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.<|reference_end|>
@article{grigoriadou2024ails-ntua,
    title={AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for
  hallucination detection and analysis},
    author={Natalia Grigoriadou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos
  Stamou},
    journal={arXiv preprint arXiv:2404.01210},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01210},
    primaryClass={cs.CL}
}