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AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis
April 2, 2024, 7:52 p.m. | Natalia Griogoriadou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou
cs.CL updates on arXiv.org arxiv.org
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
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