Synonym Knowledge Graph Enhanced Language Model for Inconsistent Hallucination Detection

Neural sequence models, despite their proficiency in generating highly fluent sentences, have also exhibited a tendency to hallucinate, introducing additional content that lacks grounding in the input data, as evidenced by recent investigations. This variety of fluent yet erroneous outputs poses a significant challenge, as it is difficult for users to discern the veracity of the presented content and identify inaccuracies. Several methods have been proposed to address this challenge. However, they sometimes detect the synonym of the true token in the generation as hallucination. To alleviate the above problem, we propose a novel architecture, i.e. Synonym Knowledge Graph Enhanced Language Model (SKGELM). We construct and prune a synonym knowledge graph according to the source sentence, which can help the subsequently graph attention network and classifier detect hallucination. Empirical results on SUMMAC benchmark and multi-domain Chinese-English translation benchmark show that our method achieves state-of-the-art performance and improves the best baseline significantly.