Mengyao Cao, Yue Dong, J. Cheung

arXiv

Paper

Abstract

State-of-the-art abstractive summarization systems often generate hallucinations ; i.e., content that is not directly inferable from the source text. Despite being assumed incorrect, many of the hallucinated contents are consis-tent with world knowledge (factual hallucinations). Including these factual hallucinations into a summary can be beneficial in provid-ing additional background information. In this work, we propose a novel detection approach that separates factual from non-factual hallucinations of entities. Our method is based on an entity’s prior and posterior probabilities according to pre-trained and finetuned masked language models, respectively. Empirical results suggest that our method vastly outperforms three strong baselines in both accuracy and F1 scores and has a strong correlation with human judgements on factuality classification tasks. Furthermore, our approach can provide insight into whether a particular hallucination is caused by the summarizer’s pre-training or fine-tuning step. 1