Before revision:  We also introduce a novel method for learning to model hallucination detection, based on pretrained language models fine tuned on synthetic data that includes automatically inserted hallucinations . Experiments on machine translation and abstract text summarization demonstrate the effectiveness of our proposed approach -- we obtain an average F1 of around 60 across all the benchmark datasets.
After revision:  We also introduce a novel method for learning to model hallucination detection, based on pretrained language models fine tuned on synthetic data that includes automatically inserted hallucinations . Experiments on machine translation (MT) and abstractive summarization demonstrate that our proposed approach -- we obtain an average F1 of around 60 across all the benchmark datasets.