AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis

arXiv:2404.01210v1 Announce Type: new
Abstract: 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 …

abstract analysis and analysis arxiv binary cases classification cs.cl detection experimentation fine-tuning hallucination hallucinations identify mistakes observable paper team type