SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection

arXiv:2404.06137v1 Announce Type: new
Abstract: 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. …

abstract arxiv cs.ai cs.cl data detection diverse ensemble hallucination investigation novel paper predictions reference standards strategies supervised learning synthetic synthetic data systems through type