2024/04/01 by Maria Lymperaiou, Grigoriadou, Natalia, Giorgos Filandrianos +4
Biochemistry, Genetics and Molecular Biology · Computer Science · #Big Data and Digital Economy #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2404.01210
openalex publication_date 2024/04/01 · openalex created_date 2024/04/03 · openalex updated_date 2026/07/28
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.